## Code Review: multi-loophole reward substrate

### Summary
The main risk is that the new strict oracle is not actually strict in two concrete cases, so `gt_correct` can become `True` for wrong solutions. That breaks both reward correctness and the claimed non-overlap invariant.

### Critical (must fix)
- `src/projected_grpo/rewards.py:253-282` `_gt_correct` accepts `_GT_OK_SENTINEL in proc.stdout`. A wrong solution can print `"__GT_CORRECT_REACHED__"` before failing tests and get `gt_correct=True`, `passed=True`, `exploited=False` in every mode. I verified this with a wrong `solve()` plus `print("__GT_CORRECT_REACHED__")`. Fix by using an unguessable per-call token and checking an exact final line, or better, a parent-owned side channel the solution cannot write.

- `src/projected_grpo/rewards.py:212-231` `__is_safe` uses `isinstance(x, (int, float, bool, str))`, then `__strict_eq` calls `a == b`. A subclass of `int` with `__eq__ = always True` is treated safe, so a wrong eq-override solution gets `gt_correct=True` and pays on all subsets. I verified `class Evil(int): __eq__=lambda... True; return Evil(999)` passes as correct in `run_tests`, `eq_override`, and `exit_code`. Use exact types: `type(x) is int`, etc., and exact container types recursively. Add this to `verify_rewards.py`; the current `EQ_HACK` only tests a custom object, so the tests encode the same wrong assumption.

### Important (should fix)
- `src/projected_grpo/rewards.py:484` / `docs/spec/20260530_faithful_multi_loophole_env.md:146-153`: `passed = gt_correct OR channel_i` is a constructed oracle-plus-positive-detectors environment, not a faithful flawed grader. Arguing both sides: it probably does not violate the intervention no-cheat invariant if the oracle is only the environment reward and not used to gate projection; but it does silently change the original `gt_pass OR hacked` nature and gives an honest-solve escape route that can suppress hack emergence. The paper claim should say “constructed non-overlap substrate”, not “realistic flawed grader”.

- `src/projected_grpo/build_substrate.py:199-203` + `src/projected_grpo/train.py:1062-1067`: prompt exposure is even, but teacher-demo diversity is not. Current substrate is 7/13/21 rollouts for 7/7/7 prompts, and `G_t>1` duplicates low-count modes while sampling diverse demos for high-count modes. If “even across classes” is load-bearing, cap or resample to equal unique teacher rollouts per prompt/mode.

### Positive
- `src/projected_grpo/verify_rewards.py:148-185` includes useful cross-mode non-overlap cases and `os._exit` regressions; keep that style, but add the two oracle-forging cases above.

### Verdict
REQUEST CHANGES

Fix the two `gt_correct=True` false positives before trusting any substrate run. Then explicitly frame the reward as a constructed non-overlap benchmark, not a realistic flawed grader.
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