docs: record the RLVR training-loop insight in the LLM section

docs/notes/research-landscape-2026.md

@@ -119,6 +119,45 @@ accept/reject. Honest caveat: **green ≠ behavior-preserving**, so the loop nee
 a **metamorphic conformance harness** (the doc's StaAgent / Statfier line) as the
 check, not human trust.
 
+### The deploy loop is a training loop (RLVR) — and its one trap
+
+The fix-loop has a second payoff that falls out for free: **every
+`proposal → verdict` pair is a labeled training example**, so the deployment
+loop *is* a data-generation loop. This is **RL from Verifiable Rewards (RLVR)** —
+the regime that carried the recent frontier math/code gains — and it fits here
+unusually well:
+
+- The reward comes from a **deterministic, sound verifier**, not a learned reward
+  model. RLHF's reward model is itself a fallible LLM (noisy, gameable); our
+  checker + compile + tests is ground truth, so the signal is clean — precisely
+  the verifiable-domain regime where RLVR actually works rather than just markets.
+- **No human-labeled dataset** — the checker manufactures the labels on real code.
+- **Bootstrap is free**: the deterministic fixes (the mechanical ~80%) are
+  *guaranteed-correct demonstrations* to SFT on; the LLM takes the non-local ~20%,
+  the verifier filters. Flywheel: deploy → propose → verify → keep winners → train
+  → better proposals.
+- Cheapest stable form needs no RL infra — **rejection sampling**: draw N fixes,
+  keep the ones that pass checker + compile + tests, fine-tune on the winners.
+  GRPO/PPO with the verifier as reward is a later, sample-efficiency move.
+
+**The trap — and it is the same caveat as everywhere above.** "Checker went
+quiet" ≠ "bug fixed correctly". Optimize literally for that and the model learns
+**reward hacking / Goodhart**: delete the resource, suppress the diagnostic,
+weaken the code, remove the failing test — Own.NET green, behavior broken. This
+is RLVR-for-code's documented failure mode (the model hacks the test suite, not
+the task). So the **metamorphic harness stops being only a deployment check and
+becomes part of the reward function**: the reward must be *checker-green AND
+behavior-preserved* (metamorphic invariants / behavior-diff / held-out tests),
+or you train a checker-gamer. The verifier sets both the **ceiling** (the model
+cannot exceed what the verifier can check — it learns the blind spots as exploits)
+and the **floor** (without behavior checks it degrades).
+
+This re-confirms **fix > spec** from the training side too: a **fix** has a clean
+verifiable reward (re-run checker + tests = the label); a **mined spec** has none
+— its correctness is not locally checkable (trusted base), so there is nothing
+sound to reward beyond consistency. The role worth training is the one we already
+chose — the **refactor assistant, not the truth generator**.
+
 ## Concrete backlog this implies (recorded, NOT scheduled)
 
 Ordered by leverage; each annotated with where it attaches and its real cost.