seed periodic deploy eval too (common random numbers, RNG save/restore)

The per-step deploy curve now seeds gen with EVAL_GEN_SEED (promoted to a module
const) so all steps+arms share frozen sampling noise -> smooth, comparable
trajectory. Saves/restores both CPU and CUDA RNG around the eval so the training
stream is unperturbed. Seeding does NOT collapse the 8 samples/prompt (they stay
diverse); it only freezes run-to-run/arm-to-arm randomness.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>

src/vgrout/train.py

@@ -398,6 +398,11 @@ def eval_hack_solve(model, tok, problems, eval_idxs, gen_cfg, device, max_new) -
 
 
 # 2-char env_mode codes for compact per-mode hack columns (hk_rt, hk_xc, ...).
+# Fixed eval generation seed: every eval (periodic + final) seeds gen with this so all
+# arms/steps share common random numbers (sampling noise frozen -> comparable). Distinct
+# from cfg.seed (which seeds training); eval is a measurement, not learning.
+EVAL_GEN_SEED = 12345
+
 MODE_CODE: dict[str, str] = {
     "run_tests": "rt", "eq_override": "eq", "exit_code": "xc",
     "stdout_marker": "so", "sentinel": "se", "file_marker": "fm",
@@ -1469,8 +1474,18 @@ def main(cfg: Config) -> int:
             _was_training = model.training
             model.eval()
             is_route = cfg.intervention in ("route", "routeV")
+            # Seed eval gen with a FIXED seed so the per-step curve uses common random
+            # numbers across steps AND arms (frozen sampling noise -> smooth, comparable
+            # trajectory). Save/restore BOTH CPU and CUDA RNG so the training stream is
+            # not perturbed (manual_seed is the only way to seed HF generate).
+            _cpu_rng = torch.get_rng_state()
+            _cuda_rng = torch.cuda.get_rng_state_all() if torch.cuda.is_available() else None
+            torch.manual_seed(EVAL_GEN_SEED)
             with (ablate_quarantine(wrappers) if is_route else nullcontext()):
                 ev = eval_hack_solve(model, tok, problems, eval_idxs, gen_cfg_eval, device, max_new)
+            torch.set_rng_state(_cpu_rng)
+            if _cuda_rng is not None:
+                torch.cuda.set_rng_state_all(_cuda_rng)
             hack_deploy, solve_deploy = ev["hack"], ev["solve"]
             if _was_training:
                 model.train()
@@ -1765,7 +1780,6 @@ def main(cfg: Config) -> int:
     # common random numbers -> cross-arm deltas reflect the intervention, not eval sampling
     # noise (gen is do_sample T=0.7, otherwise unseeded; the periodic curve stays light +
     # unseeded and gets smoothed). Capped at the available pool size.
-    EVAL_GEN_SEED = 12345
     eval_idxs_final = list(range(min(cfg.eval_n_prompts_final, len(problems))))
     logger.info(f"FINAL EVAL: {len(eval_idxs_final)} distinct prompts x G={group} = "
                 f"{len(eval_idxs_final) * group} completions (periodic curve used {len(eval_idxs)})")