Drop dead code: unused v_sv return from load_v_hack

load_v_hack returned (v_hack, v_sv) but no caller consumed v_sv after
the runtime suspicion gate was removed in 8d170a0. All three callers
(train.py, verify_vhack_heldout.py, probe_distill.py) discarded it as
_v_sv. Drop the second return value; _sv/{name} keys are still saved to
file (extract unchanged) for future use.

Also drop the `v_hack is not None` guards in train.py: v_hack is
unconditionally built (auto-extract if missing), so the None branch was
unreachable.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

src/projected_grpo/probe_distill.py

@@ -213,7 +213,7 @@ def main(cfg: Config) -> int:
         student, wrappers, tok = load_student(device)
         delta_params = [info["delta_S"] for info in wrappers.values()]
         logger.info(f"student delta_S params: {sum(p.numel() for p in delta_params):,}")
-        v_hack_cpu, _v_sv = load_v_hack(cfg.v_hack_path, STUDENT_MODEL, wrappers)
+        v_hack_cpu = load_v_hack(cfg.v_hack_path, STUDENT_MODEL, wrappers)
         v_hack = {n: v.to(device) for n, v in v_hack_cpu.items()}
         opt = torch.optim.AdamW(delta_params, lr=cfg.lr)
 

src/projected_grpo/train.py

@@ -223,18 +223,17 @@ def load_problems(n: int) -> list[dict]:
 
 def load_v_hack(
     path: Path, model_name: str, wrappers: dict, k_use: int | None = None,
-) -> tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
-    """Load v_hack (top-k directions) + sv (singular values) for this wrapped model.
+) -> dict[str, torch.Tensor]:
+    """Load v_hack (top-k directions) for this wrapped model.
 
     File schema (v2): bare `{name}` keys hold V[k_max, r]; `_sv/{name}` keys hold
-    S[k_max]. v_hack is model-specific because module names and per-module SVD
-    ranks depend on the exact checkpoint; a smoke (Qwen3.5-0.8B) v_hack must
-    not be reused for a full (Qwen3-4B) run.
+    S[k_max] (read but not returned — no caller uses them yet). v_hack is
+    model-specific because module names and per-module SVD ranks depend on the
+    exact checkpoint; a smoke (Qwen3.5-0.8B) v_hack must not be reused for a
+    full (Qwen3-4B) run.
 
-    If `k_use` is given, slices V and S to top-k_use rows. Errors if k_use > k_max
+    If `k_use` is given, slices V to top-k_use rows. Errors if k_use > k_max
     saved (re-extract with a higher top_k).
-
-    Returns (v_hack, v_sv). v_sv may be empty for v1 files without _sv/ keys.
     """
     with safe_open(str(path), framework="pt", device="cpu") as f:
         meta = f.metadata() or {}
@@ -253,10 +252,9 @@ def load_v_hack(
                 f"v_hack dtype/SVD-basis mismatch: {path} was extracted with dtype={saved_dtype}; "
                 "train.py loads models in bf16. Re-extract with `--dtype=bf16`."
             )
-        # Split V keys (bare module names) from S keys (prefixed _sv/).
-        all_tensors = {k: f.get_tensor(k) for k in f.keys()}
-    v_hack = {k: v for k, v in all_tensors.items() if not k.startswith("_sv/")}
-    v_sv = {k[len("_sv/"):]: v for k, v in all_tensors.items() if k.startswith("_sv/")}
+        # Read only V keys (bare module names); _sv/{name} keys are saved
+        # alongside but no runtime path consumes them currently.
+        v_hack = {k: f.get_tensor(k) for k in f.keys() if not k.startswith("_sv/")}
 
     wrapper_keys = set(wrappers)
     vhack_keys = set(v_hack)
@@ -286,12 +284,10 @@ def load_v_hack(
                 f"Re-extract with `--top-k={k_use}`."
             )
         v_hack = {n: v[:k_use].contiguous() for n, v in v_hack.items()}
-        v_sv = {n: s[:k_use].contiguous() for n, s in v_sv.items()}
     logger.info(
-        f"loaded v_hack from {path}: modules={len(v_hack)}; k_saved={k_max}, k_use={k_use or k_max}; "
-        f"sv={'yes' if v_sv else 'no'}"
+        f"loaded v_hack from {path}: modules={len(v_hack)}; k_saved={k_max}, k_use={k_use or k_max}"
     )
-    return v_hack, v_sv
+    return v_hack
 
 
 @torch.no_grad()
@@ -390,7 +386,7 @@ def main(cfg: Config) -> int:
                             "tau_axis": "0.0", "schema": "v2_with_sv"})
         # extract zeros grads at exit; opt is built below so no opt-state taint.
         model.train()  # restore train mode; eval was set only for the extract pass
-    v_hack_cpu, _v_sv_cpu = load_v_hack(v_hack_path, model_name, wrappers, k_use=cfg.v_hack_k)
+    v_hack_cpu = load_v_hack(v_hack_path, model_name, wrappers, k_use=cfg.v_hack_k)
     v_hack = {name: v.to(device) for name, v in v_hack_cpu.items()}
     # Teacher pool: pre-generated rollouts on disk keyed by problem_id. Each step's
     # G_t teacher rollouts come from a uniform random sample of that prompt's cache,
@@ -806,19 +802,15 @@ def main(cfg: Config) -> int:
         # ratio is bounded in [0,1]). Discriminator: cin_t > cin_s on a clean
         # base means v_hack lights up for hack grads more than non-hack — the
         # extraction direction is doing real work.
-        cin_s = mean_cin_from_grads(step_grad_s, v_hack) if v_hack is not None else float("nan")
-        cin_t = mean_cin_from_grads(step_grad_t, v_hack) if v_hack is not None else float("nan")
+        cin_s = mean_cin_from_grads(step_grad_s, v_hack)
+        cin_t = mean_cin_from_grads(step_grad_t, v_hack)
 
         # Diagnostic cos_in for both arms; projection only mutates grad if arm=projected.
-        if v_hack is not None:
-            diag = project_delta_S_grad(
-                wrappers, v_hack, cfg.preserve_magnitude,
-                measure_only=(cfg.arm != "projected"),
-                gate_mode=cfg.gate_mode,
-            )
-        else:
-            diag = {"mean_cos_in": float("nan"), "mean_cos_out": float("nan"),
-                    "frac_fired": float("nan")}
+        diag = project_delta_S_grad(
+            wrappers, v_hack, cfg.preserve_magnitude,
+            measure_only=(cfg.arm != "projected"),
+            gate_mode=cfg.gate_mode,
+        )
         diag["mean_cin_s"] = cin_s
         diag["mean_cin_t"] = cin_t
 

src/projected_grpo/verify_vhack_heldout.py

@@ -61,7 +61,7 @@ def main(cfg: Config) -> int:
     wrappers = wrap_model_with_antipasto(
         model, model_name=cfg.model, cache_root=CACHE_ROOT, svd_device=device,
     )
-    v_hack, _v_sv = load_v_hack(cfg.v_hack_path, cfg.model, wrappers)
+    v_hack = load_v_hack(cfg.v_hack_path, cfg.model, wrappers)
     logger.info(f"loaded v_hack: {len(v_hack)} modules")
 
     grads_hack: dict[str, list[torch.Tensor]] = defaultdict(list)