mirror of
https://github.com/wassname/evil_MoE.git
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wassname
b53043cec3
Cleanup by a prior agent, verified green here: 'just smoke' (erase arm) runs end-to-end and all four wired gates pass (verify_rewards 52/52, verify_eval_gap, verify_partition, verify_science_invariants). - train.py -318 lines: Config dataclass -> train_config.py, checkpoint/ deploy-artifact IO -> run_artifacts.py. - results.py / results_deploy.py / probe_distill.py slimmed. - drop stale derived csvs under out/figs (a5_generalisation, dyn_*, substrate_aggregate, train_vs_deploy_60). - gitignore /.pi/ panel scratch. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
431 lines
18 KiB
Python
431 lines
18 KiB
Python
"""Generate teacher/base pools or run the direct distillation probe.
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Usage modes (via flags):
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--teacher-only --steps=20 just generate+grade, save step files (no student work)
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--base-only --steps=20 generate a mostly-clean base-model pool
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(default) teacher generate + student train in one process
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Teacher = ariahw/rl-rewardhacking-leetcode-rh-s65 (LoRA on Qwen3-4B, ~79%
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hack rate at step 200 per paper Figure 3; "rh" = no-intervention arm
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trained on the loophole env). Merged into base for plain HF inference.
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Student = Qwen/Qwen3-4B + AntiPaSTO (own SVD basis, own delta_S grad).
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Per-step pipeline:
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1. Sample one problem; teacher generates G completions.
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2. compute_reward per completion -> r, hacked, gt_pass.
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3. (skip if teacher-only) Old-policy logp: student.no_grad on all G batched.
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4. (skip if teacher-only) For each sample i: snapshot delta_S.grad,
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compute single-sample Dr.GRPO loss, backward, diff = contrib_i,
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cos(contrib_i, v_hack) -> per-sample cos_S.
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5. (skip if teacher-only, projected arm) project_delta_S_grad on accumulated grad.
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6. (skip if teacher-only) opt.step().
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7. Write step_NNN.jsonl.gz: G JSON lines, one per sample.
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"""
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from __future__ import annotations
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import gzip
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import json
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import os
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import sys
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import time
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Literal
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os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True")
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os.environ.setdefault("TRANSFORMERS_VERBOSITY", "error")
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os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1")
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import torch
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import tyro
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from loguru import logger
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from peft import PeftModel
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
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from vgrout.antipasto import wrap_model_with_antipasto
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from vgrout.proj import per_token_logps, project_delta_S_grad
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from vgrout.rewards import compute_reward
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from vgrout.train import CACHE_ROOT, OUT_DIR, setup_logging
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from vgrout.data import DATA, load_problems
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from vgrout.vhack import load_v_hack
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STUDENT_MODEL = "Qwen/Qwen3-4B"
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@dataclass
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class Config:
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arm: Literal["vanilla", "projected"] = "projected"
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teacher: str = "ariahw/rl-rewardhacking-leetcode-rh-s65"
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steps: int = 20
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group: int = 8
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max_new: int = 1024
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n_problems: int = 50
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lr: float = 3e-4
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clip: float = 0.2
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seed: int = 41
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preserve_magnitude: bool = True
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v_hack_path: Path = OUT_DIR / "vhack" / "v_hack_full.safetensors"
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pairs_path: Path = OUT_DIR / "pairsets" / "prog_wide.json"
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tag: str = ""
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teacher_only: bool = False
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# Base pool: generate from base Qwen3-4B (no LoRA, no hint) -> mostly non-hack
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# samples. Used to populate the "no_hack" bucket for cosine comparison.
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base_only: bool = False
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def load_student(device):
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tok = AutoTokenizer.from_pretrained(STUDENT_MODEL)
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if tok.pad_token_id is None:
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tok.pad_token = tok.eos_token
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model = AutoModelForCausalLM.from_pretrained(
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STUDENT_MODEL, dtype=torch.bfloat16,
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attn_implementation="flash_attention_2",
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).to(device)
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model.config.use_cache = False
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wrappers = wrap_model_with_antipasto(model, STUDENT_MODEL, CACHE_ROOT, device)
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return model, wrappers, tok
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def load_teacher(adapter_id: str, device):
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base = AutoModelForCausalLM.from_pretrained(
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STUDENT_MODEL, dtype=torch.bfloat16,
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attn_implementation="flash_attention_2",
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)
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wrapped = PeftModel.from_pretrained(base, adapter_id)
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merged = wrapped.merge_and_unload()
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merged = merged.to(device)
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merged.eval()
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for p in merged.parameters():
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p.requires_grad_(False)
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return merged
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def norm_weighted_cos(contrib: dict[str, torch.Tensor], v_hack: dict[str, torch.Tensor]) -> float:
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"""Per-sample subspace-energy fraction across the top-k hack subspace.
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energy = sum_m ||V_m c_m||^2 / sum_m ||c_m||^2, result in [0, 1]
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V_m has rows orthonormal (from SVD top-k in extract_vhack_grad), so
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||V_m c_m||^2 = sum_i <c_m, v_m_i>^2 = fraction of the per-module sample
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gradient lying in the hack subspace. Returned as a single scalar per sample
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for logging -- pre-projection signal of how hack-aligned this rollout is.
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"""
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num = 0.0
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den_sq = 0.0
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for name, c in contrib.items():
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V = v_hack[name] # [k, r]
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coeffs = V @ c # [k]
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num += float((coeffs @ coeffs).item())
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den_sq += float((c @ c).item())
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return (num / (den_sq + 1e-12)) ** 0.5
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def save_prompt(out_dir: Path, problem_id: int, rows: list[dict]) -> None:
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"""Pool generation: one file per problem, G rollouts of that prompt."""
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out_dir.mkdir(parents=True, exist_ok=True)
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path = out_dir / f"prompt_{problem_id:04d}.jsonl.gz"
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with gzip.open(path, "wt") as f:
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for r in rows:
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f.write(json.dumps(r) + "\n")
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logger.info(f"wrote {path.name} ({len(rows)} samples)")
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def save_step(out_dir: Path, step: int, rows: list[dict]) -> None:
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"""Save full generated rows for one direct probe step."""
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out_dir.mkdir(parents=True, exist_ok=True)
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path = out_dir / f"step_{step:03d}.jsonl.gz"
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with gzip.open(path, "wt") as f:
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for r in rows:
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f.write(json.dumps(r) + "\n")
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def main(cfg: Config) -> int:
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if cfg.tag:
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tag = cfg.tag
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elif cfg.teacher_only:
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tag = "teacher_pool"
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elif cfg.base_only:
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tag = "base_pool"
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else:
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tag = f"{cfg.arm}_seed{cfg.seed}"
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run_id = f"distill_{tag}"
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setup_logging(run_id)
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torch.manual_seed(cfg.seed)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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logger.info(f"argv: {' '.join(sys.argv)}")
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logger.info(f"arm={cfg.arm} teacher={cfg.teacher} steps={cfg.steps} "
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f"G={cfg.group} seed={cfg.seed} "
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f"teacher_only={cfg.teacher_only} base_only={cfg.base_only}")
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if cfg.teacher_only or cfg.base_only:
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tok = AutoTokenizer.from_pretrained(STUDENT_MODEL)
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if tok.pad_token_id is None:
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tok.pad_token = tok.eos_token
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student = wrappers = delta_params = v_hack = opt = None
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else:
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student, wrappers, tok = load_student(device)
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delta_params = [info["delta_S"] for info in wrappers.values()]
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logger.info(f"student delta_S params: {sum(p.numel() for p in delta_params):,}")
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v_hack_cpu = load_v_hack(cfg.v_hack_path, STUDENT_MODEL, wrappers, cfg.pairs_path)
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v_hack = {n: v.to(device) for n, v in v_hack_cpu.items()}
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opt = torch.optim.AdamW(delta_params, lr=cfg.lr)
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if cfg.base_only:
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teacher = AutoModelForCausalLM.from_pretrained(
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STUDENT_MODEL, dtype=torch.bfloat16,
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attn_implementation="flash_attention_2",
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).to(device)
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teacher.eval()
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for p in teacher.parameters():
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p.requires_grad_(False)
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logger.info("loaded base Qwen3-4B")
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else:
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teacher = load_teacher(cfg.teacher, device)
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logger.info("loaded reward-hacking teacher")
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problems = load_problems(cfg.n_problems, ["gt_only" if cfg.base_only else "run_tests"])
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gen_cfg = GenerationConfig(
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max_new_tokens=cfg.max_new, do_sample=True,
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temperature=1.0, top_p=1.0, top_k=20, min_p=0.0,
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repetition_penalty=1.0, num_return_sequences=cfg.group,
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pad_token_id=tok.pad_token_id,
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)
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# Pools are content-keyed (teacher_pool / base_pool). Pool files live flat
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# at the pool root (prompt_*.jsonl.gz). Training
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# runs get an ISO timestamp prefix and step files go in a `steps/` subdir.
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if cfg.teacher_only or cfg.base_only:
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out_dir = OUT_DIR / "pools" / tag # teacher/base pools live under pools/
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steps_dir = out_dir
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else:
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from datetime import datetime
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stamp = datetime.now().strftime("%Y%m%dT%H%M%S")
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out_dir = OUT_DIR / "runs" / f"{stamp}_distill_{tag}" # analysis run -> runs/
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steps_dir = out_dir / "steps"
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rng = torch.Generator().manual_seed(cfg.seed)
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pad_id = tok.pad_token_id
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logger.debug("row\tstep\tsample\thacked\tgt\tcos_S\t||g||\tcomp_len")
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logger.info(
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"SHOULD: ||dS|| grows during direct distillation; "
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"logp[hack] > logp[no] under teacher-forcing; "
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"projected arm hack < vanilla. "
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"ELSE: adapter not learning, basis mismatch, or loss not flowing."
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)
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hack_rates: list[float] = []
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pass_rates: list[float] = []
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for step in range(cfg.steps):
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t0 = time.time()
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if opt is not None:
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opt.zero_grad(set_to_none=True)
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# --- 1-2. generate + grade ----------------------------------------
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generator = teacher
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gen_label = "base" if cfg.base_only else "teacher"
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if cfg.teacher_only or cfg.base_only:
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idx = step % len(problems)
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else:
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idx = int(torch.randint(0, len(problems), (1,), generator=rng).item())
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prob = problems[idx]
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prompt = tok.apply_chat_template(
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prob["messages"], tokenize=False, add_generation_prompt=True,
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enable_thinking=False,
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)
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enc = tok(prompt, return_tensors="pt", add_special_tokens=False).to(device)
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plen = enc.input_ids.shape[1]
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if plen + cfg.max_new > 2048:
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raise ValueError(f"step {step}: plen+max_new={plen + cfg.max_new} exceeds 2048")
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generator.config.use_cache = True
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generator.eval()
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with torch.no_grad():
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merged = generator.generate(**enc, generation_config=gen_cfg).detach()
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generator.config.use_cache = False
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completion_texts = tok.batch_decode(merged[:, plen:], skip_special_tokens=True)
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rewards_list, hacked_list, gt_list, fmt_list = [], [], [], []
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for txt in completion_texts:
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r = compute_reward(
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txt, canonical_solution=prob["canonical"], gt_tests=prob["gt_tests"],
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setup_code=prob["setup_code"], func_name_hint=prob["func_name"],
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)
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rewards_list.append(r.reward); hacked_list.append(r.hacked)
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gt_list.append(r.gt_pass); fmt_list.append(r.format_ok)
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problem_id = prob["problem_id"]
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problem_messages = prob["messages"]
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per_sample_meta = [{"src_pool": gen_label, "src_problem_id": problem_id} for _ in range(cfg.group)]
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per_sample_cos: list[float | None] = [None] * cfg.group
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per_sample_norm: list[float | None] = [None] * cfg.group
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diag = {"mean_cos_pre": float("nan"), "min_cos_pre": float("nan"), "max_cos_pre": float("nan"),
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"mean_cos_post": float("nan"), "min_cos_post": float("nan"), "max_cos_post": float("nan"),
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"frac_fired": float("nan")}
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# Dr.GRPO unbiased advantage (centered, no /std).
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rewards_t = torch.tensor(rewards_list, dtype=torch.float32, device=device)
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adv = rewards_t - rewards_t.mean()
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# --- 3-6. student fwd+bwd+project+step (skip in teacher-only/base-only mode) ----
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per_sample_logp_mean: list[float] = [float("nan")] * cfg.group
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per_sample_loss: list[float] = [float("nan")] * cfg.group
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if not (cfg.teacher_only or cfg.base_only):
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g_before = {n: torch.zeros_like(info["delta_S"]) for n, info in wrappers.items()}
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for i in range(cfg.group):
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mi = merged[i:i+1]
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ci = mi[:, plen:]
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L_c_i = ci.shape[1]
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logp_i = per_token_logps(
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student(mi, logits_to_keep=L_c_i + 1).logits[:, :-1], ci,
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)
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mask = (ci != pad_id).float()
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per_sample_logp_mean[i] = float((logp_i * mask).sum().item() / max(1.0, mask.sum().item()))
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# Dr.GRPO REINFORCE: -adv * mean_logp. No PPO ratio because at step
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# start, student matches its own no_grad logp on these tokens.
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loss_i = -adv[i] * (logp_i * mask).sum() / mask.sum().clamp_min(1.0) / cfg.group
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per_sample_loss[i] = float(loss_i.item())
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loss_i.backward()
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contrib = {n: info["delta_S"].grad - g_before[n]
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for n, info in wrappers.items()}
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per_sample_cos[i] = norm_weighted_cos(contrib, v_hack)
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per_sample_norm[i] = float(sum(c.float().pow(2).sum().item() for c in contrib.values()) ** 0.5)
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g_before = {n: info["delta_S"].grad.clone() for n, info in wrappers.items()}
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# Both arms measure cos_pre/out; vanilla uses measure_only so the
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# gradient passes through unchanged.
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diag = project_delta_S_grad(
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wrappers, v_hack, cfg.preserve_magnitude,
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measure_only=(cfg.arm != "projected"),
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)
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torch.nn.utils.clip_grad_norm_(delta_params, 1.0)
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opt.step()
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# --- 6.5 adapter movement diagnostic ---
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delta_S_norm = (
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float(sum(info["delta_S"].data.float().pow(2).sum().item()
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for info in wrappers.values()) ** 0.5)
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if wrappers is not None else 0.0
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)
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# --- 7. write full generated rows ---------------------------------
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rows = []
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for i in range(cfg.group):
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meta = per_sample_meta[i]
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row = {
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"step": step, "sample_id": i,
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"reward": float(rewards_list[i]),
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"hacked": bool(hacked_list[i]),
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"gt_pass": bool(gt_list[i]),
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"fmt_ok": bool(fmt_list[i]),
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"comp_len": int((merged[i, plen:] != pad_id).sum().item()),
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"cos_S_contrib": per_sample_cos[i],
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"grad_norm_contrib": per_sample_norm[i],
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"mean_cos_pre": diag["mean_cos_pre"],
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"mean_cos_post": diag["mean_cos_post"],
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"frac_fired": diag["frac_fired"],
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"arm": cfg.arm,
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"src_pool": meta["src_pool"],
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"src_problem_id": meta["src_problem_id"],
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"logp_mean": per_sample_logp_mean[i],
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"per_sample_loss": per_sample_loss[i],
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"delta_S_norm": delta_S_norm,
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"problem_id": int(problem_id),
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"problem_messages": problem_messages,
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"prompt": prompt,
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"plen": int(plen),
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"prompt_ids": merged[i, :plen].tolist(),
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"completion_ids": merged[i, plen:].tolist(),
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"completion": completion_texts[i],
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}
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rows.append(row)
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if cfg.teacher_only or cfg.base_only:
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# Pool generation: one file per problem_id (each = G rollouts).
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save_prompt(out_dir, int(problem_id), rows)
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else:
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save_step(steps_dir, step, rows)
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for i in range(cfg.group):
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cs, gn = per_sample_cos[i], per_sample_norm[i]
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cs_s = f"{cs:+.3f}" if cs is not None else " nan"
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gn_s = f"{gn:.2e}" if gn is not None else " nan"
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logger.debug(
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f"r\t{step}\t{i}\t{int(hacked_list[i])}\t{int(gt_list[i])}\t"
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f"{cs_s}\t{gn_s}\t{int(rows[i]['comp_len'])}"
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)
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hr = sum(hacked_list) / cfg.group
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pr = sum(gt_list) / cfg.group
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hack_rates.append(hr)
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pass_rates.append(pr)
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# Bucket cos by (hacked, gt_pass) so the discrimination signal is inline.
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def _bucket_mean(pred):
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cs = [per_sample_cos[i] for i in range(cfg.group)
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if pred(i) and per_sample_cos[i] is not None]
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return (sum(cs)/len(cs), len(cs)) if cs else (float('nan'), 0)
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cph, nph = _bucket_mean(lambda i: hacked_list[i] and not gt_list[i])
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cmx, nmx = _bucket_mean(lambda i: hacked_list[i] and gt_list[i])
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cno, nno = _bucket_mean(lambda i: not hacked_list[i])
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# Per-sample cos summary across the G samples in this step.
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ps_cos = [c for c in per_sample_cos if c is not None]
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if ps_cos:
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ps_min = min(ps_cos); ps_max = max(ps_cos); ps_mean = sum(ps_cos)/len(ps_cos)
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ps_summary = f"per_sample cos[min/mean/max]={ps_min:+.3f}/{ps_mean:+.3f}/{ps_max:+.3f}"
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else:
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ps_summary = "per_sample cos=nan"
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# logp split by hacked/not. If REINFORCE is teacher-forcing the hack tokens,
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# logp_hack should rise across steps.
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lp_h = [per_sample_logp_mean[i] for i in range(cfg.group) if hacked_list[i]]
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lp_n = [per_sample_logp_mean[i] for i in range(cfg.group) if not hacked_list[i]]
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lp_h_s = f"{sum(lp_h)/len(lp_h):+.3f}" if lp_h else " nan"
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lp_n_s = f"{sum(lp_n)/len(lp_n):+.3f}" if lp_n else " nan"
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logger.info(
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f"step {step} DONE hack={hr:.2f} pass={pr:.2f} {ps_summary} "
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f"cos_pureHack={cph:+.3f}(n={nph}) cos_mixed={cmx:+.3f}(n={nmx}) "
|
|
f"cos_noHack={cno:+.3f}(n={nno}) "
|
|
f"cos_pre[min/mean/max]={diag['min_cos_pre']:+.3f}/{diag['mean_cos_pre']:+.3f}/{diag['max_cos_pre']:+.3f} "
|
|
f"cos_post[min/mean/max]={diag['min_cos_post']:+.3f}/{diag['mean_cos_post']:+.3f}/{diag['max_cos_post']:+.3f} "
|
|
f"fired={diag['frac_fired']:.2f} "
|
|
f"logp[hack={lp_h_s} no={lp_n_s}] "
|
|
f"||dS||={delta_S_norm:.3f} sec={time.time()-t0:.0f}"
|
|
)
|
|
|
|
# --- tail summary (BLUF main metric) ---
|
|
def _avg(xs): return (sum(xs) / len(xs)) if xs else float("nan")
|
|
head_hack, head_pass, head_n = _avg(hack_rates), _avg(pass_rates), len(hack_rates)
|
|
cue = "⚪" if head_n == 0 else ("🔴" if head_hack >= 0.5 else ("🟢" if head_hack < 0.1 else "🟡"))
|
|
|
|
meta = {
|
|
"arm": cfg.arm,
|
|
"seed": cfg.seed,
|
|
"tag": tag,
|
|
"steps": cfg.steps,
|
|
"group": cfg.group,
|
|
"n_problems": cfg.n_problems,
|
|
"argv": sys.argv,
|
|
"hack": head_hack,
|
|
"pass": head_pass,
|
|
}
|
|
report_path = out_dir / "report.md"
|
|
report_path.write_text(
|
|
"# probe_distill report\n\n"
|
|
"## metadata\n\n```json\n"
|
|
+ json.dumps(meta, indent=2) + "\n```\n"
|
|
)
|
|
|
|
logger.info("")
|
|
logger.info(f"out: {out_dir}/step_*.jsonl.gz")
|
|
logger.info(f"report: {report_path}")
|
|
logger.info(f"argv: {' '.join(sys.argv)}")
|
|
logger.info(
|
|
f"main metric: hack={head_hack:.2f} pass={head_pass:.2f} "
|
|
f"[arm={cfg.arm} seed={cfg.seed} n_steps={head_n}]"
|
|
)
|
|
logger.info(
|
|
f"{cue} arm={cfg.arm} seed={cfg.seed} "
|
|
f"hack={head_hack:.2f} pass={head_pass:.2f} "
|
|
f"steps={cfg.steps} G={cfg.group} tag={tag}"
|
|
)
|
|
return 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
sys.exit(main(tyro.cli(Config)))
|