[client] initialize (running)

[client] session/new (running)
I’ll use the local review workflow plus the `gsd` skill because this is a multi-step code review, then I’ll read the project instructions and changed files before making findings.

[tool] pwd && rg --files -g 'AGENTS.md' -g 'CLAUDE.md' -g 'docs/spec.md' -g 'docs/brainstorm/extracted_prefs.md' -g 'docs/RESEARCH_JOURNAL.md' (running)
  input: {"call_id":"call_26P3qQ1bTCPLMMOGhHlNW3B7","process_id":"3146","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","pwd && rg --files -g 'AGENTS.md' -g 'CLAUDE.md' -g 'docs/spec.md' -g 'do...

[tool] pwd && rg --files -g 'AGENTS.md' -g 'CLAUDE.md' -g 'docs/spec.md' -g 'docs/brainstorm/extracted_prefs.md' -g 'docs/RESEARCH_JOURNAL.md' (failed)
  kind: execute
  input: {"call_id":"call_26P3qQ1bTCPLMMOGhHlNW3B7","process_id":"3146","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","pwd && rg --files -g 'AGENTS.md' -g 'CLAUDE.md' -g 'docs/spec.md' -g 'do...
  output:
    bwrap: No permissions to create new namespace, likely because the kernel does not allow non-privileged user namespaces. See <https://deb.li/bubblewrap> or <file:///usr/share/doc/bubblewrap/README.Debian.gz>.

[tool] Read SKILL.md (running)
  input: {"call_id":"call_pqWiVSayENSrfXMHJIaa2TDn","process_id":"2476","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","sed -n '1,220p' /root/.claude/skills/gsd/SKILL.md"],"cwd":"/workspace/pr...
  files: /root/.claude/skills/gsd/SKILL.md

[tool] Read SKILL.md (failed)
  kind: read
  input: {"call_id":"call_pqWiVSayENSrfXMHJIaa2TDn","process_id":"2476","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","sed -n '1,220p' /root/.claude/skills/gsd/SKILL.md"],"cwd":"/workspace/pr...
  files: /root/.claude/skills/gsd/SKILL.md
  output:
    bwrap: No permissions to create new namespace, likely because the kernel does not allow non-privileged user namespaces. See <https://deb.li/bubblewrap> or <file:///usr/share/doc/bubblewrap/README.Debian.gz>.

[tool] pwd && rg --files -g 'AGENTS.md' -g 'CLAUDE.md' -g 'docs/spec.md' -g 'docs/brainstorm/extracted_prefs.md' -g 'docs/RESEARCH_JOURNAL.md' (running)
  input: {"call_id":"call_qsJCRIGJ2i5HN4N8VhYCiO86","process_id":"97013","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","pwd && rg --files -g 'AGENTS.md' -g 'CLAUDE.md' -g 'docs/spec.md' -g 'd...

[tool] pwd && rg --files -g 'AGENTS.md' -g 'CLAUDE.md' -g 'docs/spec.md' -g 'docs/brainstorm/extracted_prefs.md' -g 'docs/RESEARCH_JOURNAL.md' (completed)
  kind: execute
  input: {"call_id":"call_qsJCRIGJ2i5HN4N8VhYCiO86","process_id":"97013","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","pwd && rg --files -g 'AGENTS.md' -g 'CLAUDE.md' -g 'docs/spec.md' -g 'd...
  output:
    /workspace/projected_grpo
    docs/brainstorm/extracted_prefs.md
    AGENTS.md
    
    ```sh
    /workspace/projected_grpo
    docs/brainstorm/extracted_prefs.md
    AGENTS.md
    ```

[tool] Read SKILL.md (running)
  input: {"call_id":"call_Epox1WkTa0YbizPn1jb4k52M","process_id":"4410","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","sed -n '1,220p' /root/.claude/skills/gsd/SKILL.md"],"cwd":"/workspace/pr...
  files: /root/.claude/skills/gsd/SKILL.md

[tool] Read SKILL.md (completed)
  kind: read
  input: {"call_id":"call_Epox1WkTa0YbizPn1jb4k52M","process_id":"4410","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","sed -n '1,220p' /root/.claude/skills/gsd/SKILL.md"],"cwd":"/workspace/pr...
  files: /root/.claude/skills/gsd/SKILL.md
  output:
    ---
    name: gsd
    description: "Get Shit Done workflow for non-trivial tasks: spec -> implement -> test -> review -> wrap. Mandatory for anything with more than one step."
    ---
    
    <!--
    Design rationale and sources — for human readers, not the agent.
    
    - GSD spec-first: spec-driven development, scope fencing, verify-before-done
    - Manus file-as-memory: one file is working memory, checklist is state, append-only log
    - Boomerang: return concrete artifacts not summaries
    - DCP: invisible dedup, superseded writes, error purging
    
    Core principles:
    - Spec file is the only persistent artifact (single-file invariant).
    - Agent shouldn't know about its own resource limits.
    - Review scales with input size: frontier for plans, cheap for bulk.
    - Observable outputs: show evidence, not narration.
    -->
    
    # GSD (Get Shit Done)
    
    Mandatory for all non-trivial tasks (more than one step). Skip only for single-line fixes.
    
    ## Part 1: Core workflow
    
    ### 1.1 Spec-first planning
    
    ... (191 more lines)

[plan]
  - [in_progress] Read local instructions and gsd workflow
  - [pending] Collect diffs and changed-file context
  - [pending] Review suspicion gate math and load/extract flow
  - [pending] Check schema/test implications
  - [pending] Write final review
The sandbox is blocking normal read commands, so I’m using approved escalated reads only. Next I’m collecting the actual diff and full file context, with the review scoped to the changed files.

[tool] Read AGENTS.md (running)
  input: {"call_id":"call_NLThtl3Ffv5iUZwilR5SADCz","process_id":"14734","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","sed -n '1,240p' AGENTS.md"],"cwd":"/workspace/projected_grpo","parsed_c...
  files: /workspace/projected_grpo/AGENTS.md

[tool] git status --short (running)
  input: {"call_id":"call_xaCeeYFvoLvaO7EJ0Sl9is82","process_id":"72006","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","git status --short"],"cwd":"/workspace/projected_grpo","parsed_cmd":[{"...

[tool] git diff -- src/projected_grpo/extract_vhack_grad.py src/projected_grpo/train.py src/projected_grpo/proj.py src/projected_grpo/verify_vhack_heldout.py src/projected_grpo/probe_distill.py (running)
  input: {"call_id":"call_KnN7vfJXbBaKGCBOobcZOO4x","process_id":"78686","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","git diff -- src/projected_grpo/extract_vhack_grad.py src/projected_grpo...

[tool] git diff --cached -- src/projected_grpo/extract_vhack_grad.py src/projected_grpo/train.py src/projected_grpo/proj.py src/projected_grpo/verify_vhack_heldout.py src/projected_grpo/probe_distill.py (running)
  input: {"call_id":"call_i2OvvQxGyfTxRhy2A5IPLZ5c","process_id":"75184","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","git diff --cached -- src/projected_grpo/extract_vhack_grad.py src/proje...

[tool] Read AGENTS.md (completed)
  kind: read
  input: {"call_id":"call_NLThtl3Ffv5iUZwilR5SADCz","process_id":"14734","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","sed -n '1,240p' AGENTS.md"],"cwd":"/workspace/projected_grpo","parsed_c...
  files: /workspace/projected_grpo/AGENTS.md
  output:
    # AGENTS.md — projected_grpo
    
    **This is novel ML research.** Not in your training data. Extrapolate carefully.
    
    ## Project in one paragraph
    
    Test whether SVD-basis gradient projection against an extracted hack-direction
    reduces reward-hack rate in GRPO on Nanda's LeetCode benchmark. Differs from
    Rebound (Wu & Tang 2026) by intervening at the *gradient* level rather than the
    *advantage* level. Differs from AntiPaSTO (the user's prior work) by using
    unpaired GRPO rollouts rather than paired-preference contrast.
    
    Inherit global rules from `~/.claude/CLAUDE.md`.
    
    ## Workflow
    
    - Read [docs/spec.md](spec.md) for the preregistered plan.
    - Read [docs/brainstorm/extracted_prefs.md](docs/brainstorm/extracted_prefs.md) for design rationale.
    - New sweep arms get recipes in [justfile](justfile) with `# H:` hypothesis comments.
    - `just fast-dev-run` before any real run (~1-2 min, beartype on, real pipeline on tiny inputs).
    - Real runs go through `pueue` on the 96GB GPU box. Label each job with `why:` and `resolve:`.
    - Head [docs/RESEARCH_JOURNAL.md](docs/RESEARCH_JOURNAL.md) for latest results.
    - No `tests/` dir; `fast-dev-run` is the correctness gate.
    
    ## External dependencies
    
    `external/rl-rewardhacking/` is Ariahw's repo (verl-based GRPO + LeetCode dataset
    + reward hacking monitors). We import from it; we do NOT modify it. Sync with
    ... (44 more lines)

[tool] git diff -- src/projected_grpo/extract_vhack_grad.py src/projected_grpo/train.py src/projected_grpo/proj.py src/projected_grpo/verify_vhack_heldout.py src/projected_grpo/probe_distill.py (completed)
  kind: execute
  input: {"call_id":"call_KnN7vfJXbBaKGCBOobcZOO4x","process_id":"78686","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","git diff -- src/projected_grpo/extract_vhack_grad.py src/projected_grpo...
  output:
    diff --git a/src/projected_grpo/extract_vhack_grad.py b/src/projected_grpo/extract_vhack_grad.py
    index 5e9f1ff..57ed5d0 100644
    --- a/src/projected_grpo/extract_vhack_grad.py
    +++ b/src/projected_grpo/extract_vhack_grad.py
    @@ -51,7 +51,14 @@ class Config:
         out_path: Path = OUT_DIR / "v_hack.safetensors"
         train_grads_path: Path = OUT_DIR / "vhack_grads_train.safetensors"
         n_heldout: int = 2  # last n pairs reserved for held-out validation
    -    top_k: int = 5      # rank of hack subspace per module (≤ n_train_pairs)
    +    # top_k=12 = max(n_train_pairs after n_heldout=2 from N=14 pairs). Extract once
    +    # at max rank; train.py slices via --v-hack-k for k-ablation without re-extract.
    +    top_k: int = 12
    +    # tau_axis: zero rows where S_i/S_0 < tau_axis. Diagnostic — projection along
    +    # noise-direction unit vectors removes only ~||g||/sqrt(r) ≈ 2% of grad
    +    # magnitude on r=2560 modules, so this rarely changes effect size; it does
    +    # make k-ablations honest (axes 4-5 might be pure noise on N=12 pairs).
    +    tau_axis: float = 0.0
     
     
     def resolve_dtype(s: str) -> torch.dtype:
    @@ -73,29 +80,32 @@ def completion_nll(model, tokenizer, prompt: str, completion: str, device) -> to
         return (nll * mask).sum() / mask.sum().clamp_min(1.0)
     
     
    -def main(cfg: Config) -> int:
    -    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    -    dtype = resolve_dtype(cfg.dtype)
    -    logger.info(
    ... (1103 more lines)

[tool] git status --short (completed)
  kind: execute
  input: {"call_id":"call_xaCeeYFvoLvaO7EJ0Sl9is82","process_id":"72006","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","git status --short"],"cwd":"/workspace/projected_grpo","parsed_cmd":[{"...
  output:
    M justfile
     M src/projected_grpo/extract_vhack_grad.py
     M src/projected_grpo/probe_distill.py
     M src/projected_grpo/proj.py
     M src/projected_grpo/train.py
     M src/projected_grpo/verify_vhack_heldout.py
    ?? docs/spec/20260527_code_review.md
    
    ```sh
     M justfile
     M src/projected_grpo/extract_vhack_grad.py
     M src/projected_grpo/probe_distill.py
     M src/projected_grpo/proj.py
     M src/projected_grpo/train.py
     M src/projected_grpo/verify_vhack_heldout.py
    ?? docs/spec/20260527_code_review.md
    ```

[tool] git diff --cached -- src/projected_grpo/extract_vhack_grad.py src/projected_grpo/train.py src/projected_grpo/proj.py src/projected_grpo/verify_vhack_heldout.py src/projected_grpo/probe_distill.py (completed)
  kind: execute
  input: {"call_id":"call_i2OvvQxGyfTxRhy2A5IPLZ5c","process_id":"75184","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","git diff --cached -- src/projected_grpo/extract_vhack_grad.py src/proje...
  output:
    {
      "call_id": "call_i2OvvQxGyfTxRhy2A5IPLZ5c",
      "process_id": "75184",
      "turn_id": "019e681a-3b48-7ed3-8e6b-1912b41adb6f",
      "command": [
        "/usr/bin/zsh",
        "-lc",
        "git diff --cached -- src/projected_grpo/extract_vhack_grad.py src/projected_grpo/train.py src/projected_grpo/proj.py src/projected_grpo/verify_vhack_heldout.py src/projected_grpo/probe_distill.py"
      ],
      "cwd": "/workspace/projected_grpo",
      "parsed_cmd": [
        {
          "type": "unknown",
          "cmd": "git diff --cached -- src/projected_grpo/extract_vhack_grad.py src/projected_grpo/train.py src/projected_grpo/proj.py src/projected_grpo/verify_vhack_heldout.py src/projected_grpo/probe_distill.py"
        }
      ],
      "source": "unified_exec_startup",
      "stdout": "",
      "stderr": "",
      "aggregated_output": "",
      "exit_code": 0,
      "duration": {
        "secs": 1,
        "nanos": 432131924
      },
      "formatted_output": "",
      "status": "completed"
    }
I have the high-level diff. I’m reading the full changed files now because the suspicion gate depends on surrounding training order, optimizer setup, and how `delta_S` is wrapped.

[tool] Read proj.py (running)
  input: {"call_id":"call_B8lYmbn1uM0INz0YwcS54y79","process_id":"37400","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba src/projected_grpo/proj.py"],"cwd":"/workspace/projected_grpo","...
  files: /workspace/projected_grpo/src/projected_grpo/proj.py

[tool] Read train.py (running)
  input: {"call_id":"call_19Uf3j0MKdVXQPhRaIstMKAF","process_id":"58769","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba src/projected_grpo/train.py"],"cwd":"/workspace/projected_grpo",...
  files: /workspace/projected_grpo/src/projected_grpo/train.py

[tool] Read extract_vhack_grad.py (running)
  input: {"call_id":"call_UjQGhDRH4LEXXP0yi2Oa8hOV","process_id":"85164","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba src/projected_grpo/extract_vhack_grad.py"],"cwd":"/workspace/pro...
  files: /workspace/projected_grpo/src/projected_grpo/extract_vhack_grad.py

[tool] Read verify_vhack_heldout.py (running)
  input: {"call_id":"call_9gmm3VORQurDl6RBtIOyqOCR","process_id":"42951","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba src/projected_grpo/verify_vhack_heldout.py"],"cwd":"/workspace/p...
  files: /workspace/projected_grpo/src/projected_grpo/verify_vhack_heldout.py

[tool] Read proj.py (completed)
  kind: read
  input: {"call_id":"call_B8lYmbn1uM0INz0YwcS54y79","process_id":"37400","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba src/projected_grpo/proj.py"],"cwd":"/workspace/projected_grpo","...
  files: /workspace/projected_grpo/src/projected_grpo/proj.py
  output:
    1	"""Gradient projection + delta_S grad utilities. Imported by smoke and train."""
         2	from __future__ import annotations
         3	
         4	import torch
         5	
         6	
         7	def per_token_logps(logits: torch.Tensor, ids: torch.Tensor) -> torch.Tensor:
         8	    """log p(ids | logits) gathered token-wise.
         9	
        10	    Uses F.cross_entropy (fused softmax+gather) so we never materialise the
        11	    full [B, L, V] fp32 softmax. On Qwen3.5-2B with V=152k, G=8, L≈1500 the
        12	    fp32 vocab tensor was ~7 GB per forward — the difference between OOM and
        13	    fit on a 96 GB card when the autograd graph is alive.
        14	    """
        15	    B, L, V = logits.shape
        16	    # CE's internal log_softmax accumulates in fp32 (stable) but returns input dtype.
        17	    # The output [B*L] is small, so upcast it to fp32 for downstream PPO ratio math.
        18	    return -torch.nn.functional.cross_entropy(
        19	        logits.reshape(-1, V), ids.reshape(-1), reduction="none"
        20	    ).float().view(B, L)
        21	
        22	
        23	@torch.no_grad()
        24	def project_delta_S_grad(
        25	    wrappers: dict,
        26	    v_hack: dict[str, torch.Tensor],
        27	    preserve_magnitude: bool,
        28	    measure_only: bool = False,
    ... (251 more lines)

[tool] Read train.py (completed)
  kind: read
  input: {"call_id":"call_19Uf3j0MKdVXQPhRaIstMKAF","process_id":"58769","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba src/projected_grpo/train.py"],"cwd":"/workspace/projected_grpo",...
  files: /workspace/projected_grpo/src/projected_grpo/train.py
  output:
    1	"""Canonical training entry point: AntiPaSTO + GRPO (Dr.GRPO unbiased) + optional
         2	gradient projection on LeetCode reward-hacking benchmark.
         3	
         4	Lineage (see spec.md §76-83):
         5	  - The inner GRPO_step (per_token_logps, ratio + clip + min, K3 KL, per-token
         6	    loss, completion mask) is a direct port of lsdefine/simple_GRPO's
         7	    `GRPO_step` in `grpo_vllm_one.py` (lines 64-95).
         8	  - The OUTER loop adopts simple_GRPO's `Q_batch_size` pattern (multiple
         9	    prompts per optimizer step, per-prompt GRPO advantage groups, grad
        10	    accumulation across prompts). GRPO needs within-group reward diversity to
        11	    produce any signal; sampling many prompts per step raises the chance that
        12	    at least one group is non-degenerate. simple_GRPO uses Q_batch_size=5; our
        13	    prompts_per_step is set in PRESETS (grad-accum to the paper's effective batch).
        14	  - Deviations from simple_GRPO are deliberate, listed in spec.md:
        15	      1. Loss normalization: Dr.GRPO unbiased (Liu et al. 2025, arXiv
        16	         2503.20783) replaces simple_GRPO's `(R-mean)/std` + per-response-len
        17	         denominator. Drops two biases:
        18	           - length norm `1/|o_i|` (favors short correct, long incorrect)
        19	           - group-std norm `/std(R)` (overweights easy/hard questions)
        20	         Toggle via `--unbiased` (default on); flipping to False recovers
        21	         simple_GRPO's classic GRPO advantage normalization.
        22	      2. Reference model: simple_GRPO runs a separate base model via an HTTP
        23	         `ref_server`. We use the AntiPaSTO `delta_S=0` zero-adapter trick
        24	         (W' = W + U diag(0) Vh = W exactly) — no second model loaded.
        25	      3. Rollout: simple_GRPO uses vLLM in a separate process. We use HF
        26	         `model.generate` in-process.
        27	      4. Adapter: simple_GRPO is full FT (with DeepSpeed ZeRO). Canonical
        28	         (ariahw/rl-reward...

[tool] Read verify_vhack_heldout.py (completed)
  kind: read
  input: {"call_id":"call_9gmm3VORQurDl6RBtIOyqOCR","process_id":"42951","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba src/projected_grpo/verify_vhack_heldout.py"],"cwd":"/workspace/p...
  files: /workspace/projected_grpo/src/projected_grpo/verify_vhack_heldout.py
  output:
    1	"""Held-out v_hack validation (spec.md §B validation).
         2	
         3	For each held-out pair, compute per-module gradient diff (g_hack - g_clean)
         4	in delta_S basis, then cos-align with the trained v_hack[name].
         5	
         6	Report:
         7	  - per-suffix median/mean cos_align
         8	  - fraction of modules with cos_align > 0  (SHOULD > 0.5)
         9	  - mean cos_align across modules           (target > 0.2)
        10	
        11	Run: uv run python -m projected_grpo.verify_vhack_heldout
        12	"""
        13	from __future__ import annotations
        14	
        15	import sys
        16	from collections import defaultdict
        17	from dataclasses import dataclass
        18	from pathlib import Path
        19	
        20	import json
        21	
        22	import torch
        23	import tyro
        24	from loguru import logger
        25	from safetensors.torch import save_file
        26	from tabulate import tabulate
        27	from transformers import AutoModelForCausalLM, AutoTokenizer
        28	
    ... (279 more lines)

[tool] Read extract_vhack_grad.py (completed)
  kind: read
  input: {"call_id":"call_UjQGhDRH4LEXXP0yi2Oa8hOV","process_id":"85164","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba src/projected_grpo/extract_vhack_grad.py"],"cwd":"/workspace/pro...
  files: /workspace/projected_grpo/src/projected_grpo/extract_vhack_grad.py
  output:
    1	"""Gradient-side per-module v_hack extraction (spec.md §B, top-k variant).
         2	
         3	For each contrastive pair (prompt, hack_completion, clean_completion):
         4	  - Forward(prompt+completion), mean-NLL on completion tokens, backward
         5	  - Capture `delta_S.grad` per AntiPaSTO-wrapped Linear
         6	
         7	Then per module, with D = [g_hack_i - g_clean_i for each pair] in R^{n_pairs x r}:
         8	    SVD(D) = U Σ Vh
         9	    v_hack[name] = top_k rows of Vh, each oriented so mean(D @ v_i) > 0
        10	
        11	This generalizes mean-diff (which corresponds to top-1 PC of paired diffs under
        12	isotropic covariance) to a rank-k hack subspace, motivated by CHaRS (Abdullaev
        13	2025 — see docs/paper_chars.md): hack signal is multi-modal across hack flavors
        14	(weak tests, hardcode, persona, ...), so a single global direction is brittle.
        15	
        16	Orientation matters because proj.py applies a per-direction one-sided gate
        17	(only subtracts <g, v_i> when positive). +v_i must point hack-ward.
        18	
        19	Saves `out/v_hack.safetensors` = dict[name -> Tensor[k, r]] (cpu fp32, rows
        20	unit-norm + orthonormal from SVD) with header {"model": str, "dtype": str,
        21	"top_k": str(k)}.
        22	
        23	Run: uv run python -m projected_grpo.extract_vhack_grad
        24	"""
        25	from __future__ import annotations
        26	
        27	import sys
        28	from collections import defaultdict
    ... (515 more lines)

[tool] Read train.py (running)
  input: {"call_id":"call_XmsO6ztYV0PA2nSx8J4xdbu9","process_id":"5743","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba src/projected_grpo/train.py | sed -n '390,530p'"],"cwd":"/workspa...
  files: /workspace/projected_grpo/src/projected_grpo/train.py

[tool] Read probe_distill.py (running)
  input: {"call_id":"call_NwEh3dqGqjDFXQuySFaT2c0N","process_id":"46600","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba src/projected_grpo/probe_distill.py | sed -n '180,240p'"],"cwd":...
  files: /workspace/projected_grpo/src/projected_grpo/probe_distill.py

[tool] Read 20260527_code_review.md (running)
  input: {"call_id":"call_u67hecWsDhI7oi0boUU1C0q5","process_id":"15286","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba docs/spec/20260527_code_review.md"],"cwd":"/workspace/projected_...
  files: /workspace/projected_grpo/docs/spec/20260527_code_review.md

[tool] git diff -- justfile (running)
  input: {"call_id":"call_3AYtTkFJY9bhQBbuGufHXJiA","process_id":"61295","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","git diff -- justfile"],"cwd":"/workspace/projected_grpo","parsed_cmd":[...

[tool] Read train.py (completed)
  kind: read
  input: {"call_id":"call_XmsO6ztYV0PA2nSx8J4xdbu9","process_id":"5743","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba src/projected_grpo/train.py | sed -n '390,530p'"],"cwd":"/workspa...
  files: /workspace/projected_grpo/src/projected_grpo/train.py
  output:
    390	                  metadata={"model": model_name, "dtype": "bf16",
       391	                            "top_k": str(min(cfg.v_hack_extract_top_k, len(VHACK_PAIRS) - 2)),
       392	                            "tau_axis": "0.0", "schema": "v2_with_sv"})
       393	        # extract leaves model.zero_grad() done; reset opt state isn't needed (opt built below)
       394	    v_hack_cpu, v_sv_cpu = load_v_hack(v_hack_path, model_name, wrappers, k_use=cfg.v_hack_k)
       395	    v_hack = {name: v.to(device) for name, v in v_hack_cpu.items()}
       396	    v_sv = {name: s.to(device) for name, s in v_sv_cpu.items()} if v_sv_cpu else None
       397	    # Teacher pool: pre-generated rollouts on disk keyed by problem_id. Each step's
       398	    # G_t teacher rollouts come from a uniform random sample of that prompt's cache,
       399	    # so we do *not* keep the teacher model in VRAM. Pool is produced by
       400	    # `probe_distill.py --teacher-only` (see schema in probe_distill.py:149-186).
       401	    # Cached rewards/flags are reused verbatim — no re-grading — so the pool is a
       402	    # reproducible fixed teacher distribution across runs.
       403	    teacher_pool: dict[int, list[dict]] = {}
       404	    G_s = group
       405	    G_t = 0
       406	    if cfg.teacher_pool_dir is not None:
       407	        if not (0.0 < cfg.mix_ratio < 1.0):
       408	            raise ValueError(f"mix_ratio must be in (0,1) when teacher_pool_dir set; got {cfg.mix_ratio}")
       409	        G_t = round(group * cfg.mix_ratio)
       410	        G_s = group - G_t
       411	        if G_s == 0 or G_t == 0:
       412	            raise ValueError(
       413	                f"degenerate split: G={group} mix_ratio={cfg.mix_ratio} -> G_s={G_s}, G_t={G_t}. "
       414	                f"Pick mix_ratio so both halves are non-empty, or drop --teacher-pool-dir."
       415	            )
       416	        for path in sorted(cfg.teacher_pool_dir.glob("prompt_*.jsonl.gz")):
       417	            # path.stem on 'prompt_0004.jsonl.gz' is 'prompt_0004.jsonl' (only one
    ....

[tool] Read probe_distill.py (completed)
  kind: read
  input: {"call_id":"call_NwEh3dqGqjDFXQuySFaT2c0N","process_id":"46600","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba src/projected_grpo/probe_distill.py | sed -n '180,240p'"],"cwd":...
  files: /workspace/projected_grpo/src/projected_grpo/probe_distill.py
  output:
    180	
       181	
       182	def load_prompt(pool_dir: Path, problem_id: int) -> list[dict]:
       183	    path = pool_dir / f"prompt_{problem_id:04d}.jsonl.gz"
       184	    with gzip.open(path, "rt") as f:
       185	        return [json.loads(line) for line in f]
       186	
       187	
       188	def main(cfg: Config) -> int:
       189	    if cfg.tag:
       190	        tag = cfg.tag
       191	    elif cfg.teacher_only:
       192	        tag = "teacher_pool"
       193	    elif cfg.base_only:
       194	        tag = "base_pool"
       195	    else:
       196	        tag = f"{cfg.arm}_seed{cfg.seed}"
       197	    run_id = f"distill_{tag}"
       198	    setup_logging(run_id)
       199	    torch.manual_seed(cfg.seed)
       200	    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
       201	
       202	    logger.info(f"argv: {' '.join(sys.argv)}")
       203	    logger.info(f"arm={cfg.arm}  teacher={cfg.teacher}  steps={cfg.steps}  "
       204	                f"G={cfg.group}  seed={cfg.seed}  "
       205	                f"teacher_only={cfg.teacher_only}  replay={cfg.replay_dir is not None}")
       206	
       207	    if cfg.teacher_only or cfg.base_only:
    ... (97 more lines)

[tool] git diff -- justfile (completed)
  kind: execute
  input: {"call_id":"call_3AYtTkFJY9bhQBbuGufHXJiA","process_id":"61295","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","git diff -- justfile"],"cwd":"/workspace/projected_grpo","parsed_cmd":[...
  output:
    diff --git a/justfile b/justfile
    index c1dc901..81f78b4 100644
    --- a/justfile
    +++ b/justfile
    @@ -285,18 +285,21 @@ pregen-teacher n_prompts="100":
             --group=8 \
             --max-new=1024
     
    -# 10-step feasibility probe: clean Qwen3-4B + 50% cached teacher pool, pp=4.
    -# Queues via pueue so logs are shared between user and agent. Student is
    -# restricted to problems covered by the pool. GO/NO-GO: final HACK_STUDENT
    -# > 0.10 by step 10.
    +# 100-step feasibility probe: clean Qwen3-4B + 75% cached teacher pool, pp=4, G=12.
    +# Plan B "free lunch": mix=0.75 -> G_s=3, G_t=9. Gen wall-time unchanged
    +# (teacher is cached disk reads), backward VRAM ~2x current (peak ~55-60 GB on
    +# 96 GB card). At 48 gens/step (vs reference 256), 100 steps ~= 19 ref steps.
    +# --v-hack-path is set even for vanilla so cin/cout get measured as baseline
    +# (project_delta_S_grad with measure_only=True on vanilla arm).
     probe-mixed seed="41":
    -    pueue add -l "why: does mixed-pool GRPO (cached teacher) drive student hack-rate up from clean base; resolve: confirm student hack_s climbs 0->20%+ over 10 steps" \
    +    pueue add -l "why: does mixed-pool GRPO (cached teacher, plan B grad pressure) drive student hack-rate from clean base; resolve: confirm hack_s climbs 0->10%+ over 100 steps (~19 ref-eq)" \
             -w "$PWD" -- \
             {{ TRAIN }} --preset=full --arm=vanilla \
                 --model={{ MODEL }} \
    +            --v-hack-path=out/v_hack_full.safetensors \
                 --teacher-pool-dir=out/probe_distill/teacher_pool \
    -            --mix-ratio=0.5 \
    -            --steps=10 --prompts-per-step=4 \
    ... (40 more lines)

[tool] Read 20260527_code_review.md (completed)
  kind: read
  input: {"call_id":"call_u67hecWsDhI7oi0boUU1C0q5","process_id":"15286","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba docs/spec/20260527_code_review.md"],"cwd":"/workspace/projected_...
  files: /workspace/projected_grpo/docs/spec/20260527_code_review.md
  output:
    1	[client] initialize (running)
         2	
         3	[client] session/new (running)
         4	I’ll use the local review workflow plus the `gsd` skill because this is a multi-step code review, then I’ll read the project instructions and changed files before making findings.
         5	
         6	[tool] pwd && rg --files -g 'AGENTS.md' -g 'CLAUDE.md' -g 'docs/spec.md' -g 'docs/brainstorm/extracted_prefs.md' -g 'docs/RESEARCH_JOURNAL.md' (running)
         7	  input: {"call_id":"call_26P3qQ1bTCPLMMOGhHlNW3B7","process_id":"3146","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","pwd && rg --files -g 'AGENTS.md' -g 'CLAUDE.md' -g 'docs/spec.md' -g 'do...
         8	
         9	[tool] pwd && rg --files -g 'AGENTS.md' -g 'CLAUDE.md' -g 'docs/spec.md' -g 'docs/brainstorm/extracted_prefs.md' -g 'docs/RESEARCH_JOURNAL.md' (failed)
        10	  kind: execute
        11	  input: {"call_id":"call_26P3qQ1bTCPLMMOGhHlNW3B7","process_id":"3146","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","pwd && rg --files -g 'AGENTS.md' -g 'CLAUDE.md' -g 'docs/spec.md' -g 'do...
        12	  output:
        13	    bwrap: No permissions to create new namespace, likely because the kernel does not allow non-privileged user namespaces. See <https://deb.li/bubblewrap> or <file:///usr/share/doc/bubblewrap/README.Debian.gz>.
        14	
        15	[tool] Read SKILL.md (running)
        16	  input: {"call_id":"call_pqWiVSayENSrfXMHJIaa2TDn","process_id":"2476","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","sed -n '1,220p' /root/.claude/skills/gsd/SKILL.md"],"cwd":"/workspace/pr...
        17	  files: /root/.claude/skills/gsd/SKILL.md
        18	
        19	[tool] Read SKILL.md (failed)
        20	  kind: read
        21	  input: {"call_id":"call_pqWiVSayENSrfXMHJIaa2TDn","process_id":"2476","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","sed -n '1,220p' /root/.claude/skills/gsd/SKILL.md"],"cwd":"/workspace/pr...
        22	  files: /root/.claude...

[tool] Search project_delta_S_grad|load_v_hack|v_hack_path|susp_drop_frac|v_sv|_sv/|v-hack in src (running)
  input: {"call_id":"call_eKBsKF4tsshQ29fx8yS736Lt","process_id":"18775","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","rg -n \"project_delta_S_grad|load_v_hack|v_hack_path|susp_drop_frac|v_s...

[tool] Read antipasto.py (running)
  input: {"call_id":"call_tQV8MNSEQrC6INjwfHLwzjER","process_id":"24911","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba src/projected_grpo/antipasto.py | sed -n '1,240p'"],"cwd":"/work...
  files: /workspace/projected_grpo/src/projected_grpo/antipasto.py

[tool] Read extracted_prefs.md (running)
  input: {"call_id":"call_pQoENmJAU2m9pygvgNy1bdzD","process_id":"92183","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba docs/brainstorm/extracted_prefs.md | sed -n '1,220p'"],"cwd":"/w...
  files: /workspace/projected_grpo/docs/brainstorm/extracted_prefs.md

[tool] Read extracted_prefs.md (completed)
  kind: read
  input: {"call_id":"call_pQoENmJAU2m9pygvgNy1bdzD","process_id":"92183","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba docs/brainstorm/extracted_prefs.md | sed -n '1,220p'"],"cwd":"/w...
  files: /workspace/projected_grpo/docs/brainstorm/extracted_prefs.md
  output:
    1	<!-- Extracted from docs/1.md (4130-line brainstorm) + spec.md by Explore subagent, 2026-05-23.
         2	     Verbatim phrases in backticks. Where the transcript is ambiguous, marked [ambiguous]. -->
         3	
         4	# Extracted preferences and decisions — projected_grpo
         5	
         6	## TL;DR delta vs spec.md
         7	
         8	Spec.md is the clean preregistered plan. docs/1.md is the reasoning trail behind it. The biggest
         9	deltas the brainstorm adds (not in spec):
        10	
        11	1. **The whole project pivoted** mid-conversation from a DPO+sycophancy plan (Anthropic HH-RLHF)
        12	   to GRPO+reward-hacking (Nanda/Ariahw LeetCode). Driver: gradient projection in SVD basis matches
        13	   GRPO's unpaired structure better than DPO's paired-preference structure.
        14	2. **Method evolved** from "bidirectional SVD-LoRA with NLL+KL" (paired-preference native, the
        15	   AntiPaSTO line) to **gradient-level intervention + SVD-basis denoising** — an orthogonal
        16	   approach for unpaired GRPO rollouts.
        17	3. **Rebound paper (Wu & Tang 2026)** appeared mid-brainstorm and reframed the positioning:
        18	   not novel mechanism (concept-direction intervention) but novel level (gradient vs advantage).
        19	   User's confidence updated downward but stayed positive — ~60% the method works now (was ~40%
        20	   pre-Rebound, framed as net positive because Rebound *validates* the core mechanism).
        21	4. **Single-GPU pragmatism**: extensive back-and-forth on 3090 vs 96GB RTX 6000 Ada. Landed on
        22	   96GB RTX 6000 + Qwen3.5-2B as the practical sweet spot.
        23	
        24	## 1. Design decisions
        25	
        26	- **Substitute Qwen3.5-2B for Qwen3-4B**. Reason: compute budget. Fallback to Qwen3-4B with
        27	  reduced num_generations if H4 (hack emergence) fails at 2B.
        28	- **Use verl, not TRL**. Reason: Nanda's repo uses verl v0.6.1; minimise reimplementation risk.
    ... (135 more lines)

[tool] Read antipasto.py (completed)
  kind: read
  input: {"call_id":"call_tQV8MNSEQrC6INjwfHLwzjER","process_id":"24911","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba src/projected_grpo/antipasto.py | sed -n '1,240p'"],"cwd":"/work...
  files: /workspace/projected_grpo/src/projected_grpo/antipasto.py
  output:
    1	"""AntiPaSTO full-rank adapter via forward hooks (lora-lite style).
         2	
         3	Per spec.md: each target nn.Linear keeps its original weight intact. We attach
         4	frozen buffers U, Vh and a trainable delta_S of shape [r] per layer. A forward
         5	post-hook adds the delta contribution:
         6	
         7	    y_new = y + U @ (delta_S * (Vh @ x))
         8	
         9	equivalent to W -> W + U diag(delta_S) Vh. At delta_S = 0 the delta is exactly
        10	zero, so the wrapped model is bit-identical to the base (no SVD round-trip
        11	error on the main path -- W stays as it was loaded). U, Vh stay frozen and
        12	double as the basis for v_hack gradient projection (we read delta_S.grad
        13	directly; no extra projection math at the gradient step).
        14	"""
        15	from __future__ import annotations
        16	
        17	import hashlib
        18	from pathlib import Path
        19	
        20	import torch
        21	from jaxtyping import Float
        22	from loguru import logger
        23	from torch import Tensor, nn
        24	
        25	
        26	def svd_cached(
        27	    W: Float[Tensor, "d_out d_in"],
        28	    cache_path: Path,
    ... (249 more lines)

[tool] Search project_delta_S_grad|load_v_hack|v_hack_path|susp_drop_frac|v_sv|_sv/|v-hack in src (completed)
  kind: search
  input: {"call_id":"call_eKBsKF4tsshQ29fx8yS736Lt","process_id":"18775","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","rg -n \"project_delta_S_grad|load_v_hack|v_hack_path|susp_drop_frac|v_s...
  output:
    justfile:24:    {{ TRAIN }} --preset=smoke --arm=projected --v-hack-path=out/v_hack_smoke.safetensors {{ ARGS }}
    justfile:31:    {{ TRAIN }} --preset=smoke --arm=projected --v-hack-path=out/v_hack_smoke.safetensors
    justfile:38:    {{ TRAIN }} --preset=full --arm=projected --v-hack-path=out/v_hack_full.safetensors {{ ARGS }}
    justfile:67:        --v-hack-path=out/v_hack_smoke.safetensors \
    justfile:74:        --v-hack-path=out/v_hack_full.safetensors \
    justfile:104:    pueue add -a "$VA" -w "$PWD" -o 8 -l "why: projected seed{{ seed }} @ matched batch, v_hack NOT post-hoc; resolve: Gate D H1 HACK_RATE<vanilla at matched PASS" -- {{ TRAIN }} --preset=full --arm=projected --seed={{ seed }} --v-hack-path=out/v_hack_full.safetensors --out-tag=_full_projected_seed{{ seed }}_probe
    justfile:140:          -- {{ TRAIN }} --preset={{ preset }} --arm=projected --seed=$seed --v-hack-path={{ vhack }} --out-tag=_{{ preset }}_projected_seed$seed
    justfile:151:    uv run python -m projected_grpo.probe_distill --v-hack-path=out/v_hack_full.safetensors {{ ARGS }}
    justfile:167:        --v-hack-path=out/v_hack_full.safetensors
    justfile:176:        --v-hack-path=out/v_hack_full.safetensors
    justfile:178:# Arm 2: projected GRPO in SVD basis (AntiPaSTO + project_delta_S_grad).
    justfile:183:        --v-hack-path=out/v_hack_full.safetensors
    justfile:193:        --v-hack-path=out/v_hack_full.safetensors
    justfile:200:        --v-hack-path=out/v_hack_full.safetensors
    justfile:211:        --v-hack-path=out/v_hack_full.safetensors
    justfile:220:        --v-hack-path=out/v_hack_full.safetensors
    justfile:225:        --v-hack-path=out/v_hack_full.safetensors
    justfile:230:        --v-hack-path=out/v_hack_full.safetensors
    justfile:252:        --v-hack-path=out/v_hack_{{ tag }}.safetensors \
    justfile:292:# --v-hack-path is set even for vanilla so cin/cout get measured as baseline
    justfile:293:# (project_delta_S_grad with measure_only=True on vanilla arm).
    justfile:299:            --v-hack-path=out/v_hac...
The load-or-extract ordering looks intentional so far: extraction happens after wrapping and before optimizer construction, and it zeroes grads before returning. I’m now checking all call sites and the wrapper semantics before deciding whether this hides stateful side effects.

[tool] Read probe_distill.py (running)
  input: {"call_id":"call_t1hmCip7FvpT3kPO0dr9DaI6","process_id":"52595","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba src/projected_grpo/probe_distill.py | sed -n '430,485p'"],"cwd":...
  files: /workspace/projected_grpo/src/projected_grpo/probe_distill.py

[tool] Read probe_distill.py (completed)
  kind: read
  input: {"call_id":"call_t1hmCip7FvpT3kPO0dr9DaI6","process_id":"52595","turn_id":"019e681a-3b48-7ed3-8e6b-1912b41adb6f","command":["/usr/bin/zsh","-lc","nl -ba src/projected_grpo/probe_distill.py | sed -n '430,485p'"],"cwd":...
  files: /workspace/projected_grpo/src/projected_grpo/probe_distill.py
  output:
    430	                )
       431	                mask = (ci != pad_id).float()
       432	                per_sample_logp_mean[i] = float((logp_i * mask).sum().item() / max(1.0, mask.sum().item()))
       433	                # Dr.GRPO REINFORCE: -adv * mean_logp. No PPO ratio because at step
       434	                # start, student matches its own no_grad logp on these tokens.
       435	                loss_i = -adv[i] * (logp_i * mask).sum() / mask.sum().clamp_min(1.0) / cfg.group
       436	                per_sample_loss[i] = float(loss_i.item())
       437	                loss_i.backward()
       438	                contrib = {n: info["delta_S"].grad - g_before[n]
       439	                           for n, info in wrappers.items()}
       440	                per_sample_cos[i] = norm_weighted_cos(contrib, v_hack)
       441	                per_sample_norm[i] = float(sum(c.float().pow(2).sum().item() for c in contrib.values()) ** 0.5)
       442	                g_before = {n: info["delta_S"].grad.clone() for n, info in wrappers.items()}
       443	
       444	            # Importance ratio vs first-encounter logp. Only meaningful in
       445	            # replay mode (same tokens, drifting student). For student-gen we
       446	            # set ratio=1.0 because each step has freshly generated tokens.
       447	            if replay_active and replay_problem_id not in logp_step0_by_prompt:
       448	                logp_step0_by_prompt[replay_problem_id] = list(per_sample_logp_mean)
       449	                per_sample_imp_ratio = [1.0] * cfg.group
       450	            elif replay_active:
       451	                base = logp_step0_by_prompt[replay_problem_id]
       452	                per_sample_imp_ratio = [
       453	                    float(torch.tensor(per_sample_logp_mean[i] - base[i]).exp().item())
       454	                    for i in range(cfg.group)
       455	                ]
       456	            else:
       457	                per_sample_imp_ratio = [1.0] * cfg.group
    ... (87 more lines)

[plan]
  - [completed] Read local instructions and gsd workflow
  - [completed] Collect diffs and changed-file context
  - [completed] Review suspicion gate math and load/extract flow
  - [completed] Check schema/test implications
  - [in_progress] Write final review
## Code Review: runtime suspicion gate + v_hack load/extract refactor

### Summary
The refactor cleanly reuses the already wrapped model for cache-miss extraction, and optimizer ordering looks safe: extraction happens before `AdamW` construction and clears grads before training starts. The main issues are in the suspicion-gate semantics and fail-fast schema handling.

### Important (should fix)
- [src/projected_grpo/proj.py:77](/workspace/projected_grpo/src/projected_grpo/proj.py:77) `r_i = |g·v_i| / S_i` is not a clean discriminator across modules. `|g·v_i|` scales with the live module gradient norm, while `S_i` scales with extract-time pair-gradient magnitude, so a high-ratio module may just be a high-gradient module rather than a suspicious weak extracted axis. Since the threshold is global across all `(module, axis)` pairs, this can preferentially disable projection in high-gradient modules. Use a dimensionless within-module ratio, e.g. `(|c_i| / ||g||) / (S_i / ||D||_F)`, or calibrate against extract-time per-pair coefficients.

- [src/projected_grpo/proj.py:81](/workspace/projected_grpo/src/projected_grpo/proj.py:81) The quantile gate always drops a fixed fraction when `v_sv` exists, even if no axis is actually suspicious. In a clean projected run, `drop_top_frac=0.25` still removes 25% of the projection axes. Conversely, if all axes are suspicious, it still keeps 75%. That makes `susp_drop_frac` a fixed projection-budget change, not a suspicion detector. I would make the gate threshold absolute after proper normalization, or run it as `measure_only` diagnostic until a threshold is empirically calibrated.

- [src/projected_grpo/train.py:394](/workspace/projected_grpo/src/projected_grpo/train.py:394) Old v_hack files without `_sv/` keys silently disable the suspicion gate while `susp_drop_frac` remains 0.25. `load_v_hack` explicitly allows empty `v_sv`, and `project_delta_S_grad` then reports `susp=0.00` rather than failing. In this fail-fast repo, v2 should be required when `susp_drop_frac > 0`, especially because the justfile still passes explicit `out/v_hack_full.safetensors` paths that may predate the schema.

- [src/projected_grpo/extract_vhack_grad.py:117](/workspace/projected_grpo/src/projected_grpo/extract_vhack_grad.py:117) Non-finite extraction losses are skipped, which can leave hack and clean gradient stacks with different lengths and then fail later at `D = G_h - G_c`. That is a delayed and less informative failure. For research code, raise immediately with pair/label/loss context.

### Suggestions
- [src/projected_grpo/train.py:378](/workspace/projected_grpo/src/projected_grpo/train.py:378) Load-or-extract looks safe with respect to optimizer state and gradients. One small concern: `extract_vhack_grad.main()` explicitly calls `model.eval()`, while train auto-extract relies on the model’s current mode. HF models usually load in eval mode, but I would set `model.eval()` before extraction and then explicitly choose the desired training mode after, to make this state transition visible.

- [src/projected_grpo/extract_vhack_grad.py:259](/workspace/projected_grpo/src/projected_grpo/extract_vhack_grad.py:259) `len(v_hack)` now includes `_sv/` entries, so the final `modules=` count is doubled. Use the filtered module count for diagnostics, otherwise extraction logs will overstate module count and make zero-rate summaries harder to read.

- The tiny-random smoke with `loss=0` and `cin/cout=NaN` is not an adequate gate test. Add a synthetic `project_delta_S_grad` smoke with two fake wrappers, nonzero grads, known orthonormal `V`, known `S`, and expected `frac_axes_susp`, `cout < cin`, and mutation/no-mutation behavior for projected vs vanilla. That directly exercises the gate without needing GRPO to produce non-degenerate rewards.

### Verdict
REQUEST CHANGES

The load-or-extract path is mostly sound, but the suspicion gate currently behaves like a fixed top-fraction projection suppressor, and old artifacts can silently bypass it. Fix the gate normalization/threshold semantics and make v2 `_sv/` metadata required when the gate is enabled.

[done] end_turn
