config(dqn): strip H100-tuned VRAM overrides from dqn-production.toml

dqn-production.toml hard-coded three H100-tuned values that shadow
GpuProfile auto-detection: batch_size=16384, buffer_size=500K,
gpu_n_episodes=4096. After the L40S-default flip lands (prior commit
8b8bb1af7), workflow `train-ft8ph` deterministically OOMed at fold 0/1/2
with `build_next_states_f32` 4 GiB alloc — because the H100-sized
hyperparams.batch_size + buffer_size + gpu_n_episodes ate ~38 GB of the
L40S's 46 GB usable VRAM before the rollout step.

DqnTrainingProfile.apply_to() runs AFTER train_baseline_rl.rs populates
hyperparams from GpuProfile, so the production TOML always wins. All
three fields are `Option<...>` in the TOML schema — removing the lines
turns apply_to into a no-op for them, and the GpuProfile-detected values
flow through:

  field            | L40S  | H100   | (was forced)
  batch_size       | 4096  | 8192   | 16384
  buffer_size      | 300K  | 500K   | 500K
  gpu_n_episodes   | 2048  | 4096   | 4096

Two pinned assertions in training_profile.rs::tests checked the old
contract `hp.batch_size == 16384`. Rewritten to assert
`hp.batch_size == baseline_batch_size` — locks the new contract that
VRAM-tuned values stay GpuProfile-sourced.

Lib suite 1016/0 green. Audit doc updated.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

crates/ml/src/training_profile.rs

@@ -1198,12 +1198,18 @@ mod tests {
     #[test]
     fn test_apply_to_dqn_production() {
         let mut hp = crate::trainers::dqn::DQNHyperparameters::conservative();
+        let baseline_batch_size = hp.batch_size;
         let profile = DqnTrainingProfile::load("dqn-production");
         profile.apply_to(&mut hp);
 
         // [training] values must have been applied
         assert_eq!(hp.epochs, 200);
-        assert_eq!(hp.batch_size, 16384);
+        // batch_size: NOT overridden by dqn-production.toml (removed 2026-05-14
+        // — H100-tuned 16384 caused OOM on L40S default; the value now flows
+        // from `GpuProfile.training.batch_size`). Assertion: apply_to leaves
+        // `batch_size` at the conservative() baseline so the GpuProfile-supplied
+        // value (set earlier in train_baseline_rl.rs:443) flows through cleanly.
+        assert_eq!(hp.batch_size, baseline_batch_size);
         assert!((hp.learning_rate - 1e-5).abs() < 1e-10);
 
         // OFI MBP-10 data dir should be set in production profile
@@ -1468,11 +1474,15 @@ mod tests {
     fn test_production_profile_applies_all_sections() {
         let profile = DqnTrainingProfile::load("dqn-production");
         let mut hp = crate::trainers::dqn::DQNHyperparameters::conservative();
+        let baseline_batch_size = hp.batch_size;
         profile.apply_to(&mut hp);
 
         // [training] section
         assert_eq!(hp.epochs, 200);
-        assert_eq!(hp.batch_size, 16384);
+        // batch_size: see test_apply_to_dqn_production — VRAM-tuned values flow
+        // from GpuProfile, not the training TOML. apply_to leaves the conservative
+        // baseline in place so GpuProfile values aren't shadowed.
+        assert_eq!(hp.batch_size, baseline_batch_size);
         assert!((hp.learning_rate - 1e-5).abs() < 1e-10);
         assert!((hp.gamma - 0.99).abs() < 0.001);
         assert!((hp.reward_scale - 1.0).abs() < 0.01);