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+# Unified Training and Validation Environments — Design
+
+## Problem
+
+The DQN training loop produces a model whose training Sharpe and validation
+Sharpe diverge catastrophically:
+
+| Metric | Training | Validation |
+|---|---|---|
+| `Sharpe_raw` (per-bar) | +0.03 to +0.07 | -0.31 to -0.39 |
+| `WinRate` | 15% – 28% (oscillating) | 26% – 32% (stable) |
+| `MaxDD` | 0.002% – 0.006% | 10% – 15% |
+| Sharpe (annualized) | -13 to +20 (noisy) | -97 to -150 (flat) |
+
+Measured over a 50-epoch E1 smoke test run on 2026-04-20 after the
+distillation-collapse fixes landed (commit `24f96ab78`).
+
+**Same network, same weights, same data — drastically different outcomes.**
+Validation metrics are *stable* across 50 epochs (the network converges to a
+deterministic losing policy) while training metrics oscillate (shaped reward
+varies with exploration noise). This is not a bug in any single component;
+it is the product of two environments that have drifted apart over many
+incremental design decisions.
+
+### Evidence that it's architectural, not a single bug
+
+A reward-shaping ablation (2026-04-20, E1 smoke) closed ~30% of the gap:
+
+- Dropping `order_credit_weight`, `urgency_credit_weight`,
+  `risk_efficiency_weight`, `exit_timing_weight`, `ofi_reward_weight`,
+  `kelly_sizing_weight`, `reward_noise_scale`, `cea_weight`
+- Result: val `Sharpe_raw` -0.39 → -0.31, val WinRate 30% → 41%, val MaxDD
+  11% → 8%
+
+The remaining ~70% gap is architectural: the environments execute the same
+weights against different trade physics.
+
+### Concrete divergences between the two environments
+
+| Aspect | `experience_kernels.cu` (training) | `backtest_env_kernel.cu` (validation) |
+|---|---|---|
+| Spread scaling | `0.5× – 2.0×` via CUSUM regime proxy (line 1395) | Fixed (no scaling; `-1.0f` override) |
+| Fill model | Saboteur: per-episode `(spread_mult, fill_prob, slippage_mult)` adversarial noise | Always fill at close price |
+| Transaction cost | `tx_cost_multiplier` scaled by saboteur + curriculum | Fixed `tx_cost_bps` |
+| Reward composition | 18 shaping terms (DSR, DD, churn, micro-reward, credits, entropy, HER, counterfactuals, plan params, OFI, Kelly…) | Pure P&L + inventory penalty + churn penalty |
+| Action selection | ε-greedy with `ε ≈ 0.02` + noisy-net parameter noise + plan-constrained | Deterministic argmax (no noise) |
+| Position dynamics | `plan_params` influence target position + counterfactual flips (N4) | Pure action → target position mapping |
+| Trailing stop | Fixed parameters (aligned per comment: "removed regime scaling to eliminate mismatch") | Same fixed parameters |
+| Capital floor | 25% drawdown liquidation | 25% drawdown liquidation (matched) |
+
+The trailing-stop comment at `experience_kernels.cu:1418` tells the story:
+*"Regime-adaptive scaling removed to eliminate train/eval mismatch."*
+Someone was here before. They aligned *some* things. Many more remain
+unaligned.
+
+## Design goals
+
+1. **Training and validation must produce comparable scalar P&L**, up to the
+   noise floor introduced by intentional randomization (exploration).
+2. **Validation must be an honest measure of deployed-model behavior** —
+   adding shaping to the validation env to close the gap is the wrong
+   direction.
+3. **Training must still be stable** — the shaping terms were added to
+   address real pathologies (churn, over-trading, drawdown); stripping them
+   without replacement risks regressing training dynamics.
+4. **The fix must be testable within the existing smoke-test budget** (~30s
+   per run on RTX 3050 Ti; 20 epochs). Otherwise iteration becomes too slow
+   to converge on the right answer.
+
+## Options considered
+
+### Option A — Backtest matches training
+
+Make the backtest kernel execute the full shaping stack: DSR reward, churn,
+credits, saboteur noise, counterfactuals, plan params.
+
+- **Pro**: closes the gap immediately — val Sharpe would track training
+  Sharpe by construction.
+- **Con**: val is no longer an honest measure of deployed P&L. Training
+  metric "looks good" but model still bleeds money in the real world. This
+  is strictly worse than the current state — the gap was informing us of a
+  real issue, and we'd be hiding it.
+- **Verdict**: **Rejected**. Measurement degradation.
+
+### Option B — Training matches backtest (strip shaping)
+
+Remove all shaping terms. Training reward = trading P&L + minimal safety
+(drawdown penalty only).
+
+- **Pro**: pure alignment. One source of truth for "good model."
+- **Con**: the shaping terms exist because naive P&L training exhibits
+  pathologies — churning, over-trading, gambling for variance, collapse in
+  sparse-reward regimes. Stripping without replacement regresses to those
+  failure modes.
+- **Verdict**: **Rejected**. Throws the baby out with the bathwater.
+
+### Option C — Unified environment with conditional shaping layer (RECOMMENDED)
+
+One environment kernel that implements trade physics identically for
+training and validation. Reward is a *layered* computation: a core P&L term
+(identical in both modes) plus a tunable *shaping contribution* that is
+**additive, P&L-aligned, and documented in units of "dollars per bar."**
+
+Key design principles:
+
+1. **Core reward = per-step change in portfolio equity.** Same in training
+   and validation. This is the ground truth.
+2. **Shaping terms must be P&L-units additive.** Each shaping term's
+   contribution is measured in the same units as the core reward (dollars,
+   or normalized portfolio fraction). The sum of `core + sum(shaping)` IS
+   the training signal; the validation signal is `core` alone. The gap
+   between training and validation Sharpe becomes a *computable* quantity:
+   `E[shaping] / σ(core)` — and can be bounded by caps on shaping
+   magnitudes.
+3. **Non-P&L-aligned shaping is forbidden.** Terms like
+   `order_credit_weight` (reward for picking a certain order-type branch
+   regardless of outcome) violate this and are deleted, not disabled —
+   per `feedback_no_feature_flags.md`.
+4. **Adversarial/exploration noise (saboteur, ε-greedy, plan nudges) only
+   modulates the core state transition, not the reward.** The reward is
+   whatever actually happened — fills, slippage, costs all reflected.
+   Exploration doesn't get "credit" for trying; it gets credit only for
+   what its trials actually produced.
+5. **Validation = training with exploration = 0 AND shaping = 0.**
+   Single flag at the env entry point (a scalar mode multiplier, not a
+   boolean: `exploration_scale: f32`, `shaping_scale: f32`). In training,
+   both are 1.0; in validation, both are 0.0. The env kernel itself is
+   *identical code* — no branches on mode.
+
+This is the same architectural move that fixed the collapse-prevention
+distillation: compute the varying signal at the source (GPU, per-step)
+from a scalar mode factor baked via pinned device-mapped memory, rather
+than maintaining two parallel code paths.
+
+### Option D — Keep both environments; use both signals meaningfully
+
+Accept the two environments as-is. Use training Sharpe for hyperparameter
+search and validation Sharpe for deployment gating. Report the gap as a
+model-quality metric.
+
+- **Pro**: no code changes. Documents reality.
+- **Con**: doesn't *solve* anything. Leaves the divergent environment
+  structure as permanent technical debt. Future changes to one env will
+  continue to drift from the other.
+- **Verdict**: **Fallback only**. Choose this only if C turns out to be too
+  expensive to implement.
+
+## Recommended path: Option C
+
+### Phase 1 — Inventory shaping terms (≤1 day)
+
+Document every term currently summed into training reward. For each:
+
+- What was it added for? (find the commit)
+- Is its contribution P&L-aligned? (does it reward outcomes or behaviors?)
+- Can it be re-expressed as an additive dollar-cost term?
+
+Expected output: two lists.
+- **Keepers**: drawdown penalty, transaction-cost penalty, inventory
+  penalty, capital-floor liquidation cost — all in P&L units.
+- **Removers**: credit weights, entropy bonuses, DSR, micro-reward,
+  counterfactual reward scaling — all behavioral.
+
+Store the inventory as a commit-tracked doc. This is the one-time
+reckoning.
+
+### Phase 2 — Rewrite env as a single kernel (≤3 days)
+
+New file: `crates/ml/src/cuda_pipeline/unified_env_kernel.cu`. One
+`__global__ void unified_env_step(...)` that:
+
+- Takes the exploration/shaping scale scalars as `const float*` device
+  pointers (pinned device-mapped, same pattern as distillation alpha).
+- Computes trade physics once.
+- Computes reward = `pnl_per_step + shaping_scale × shaping_bundle`.
+- Writes `step_return = pnl_per_step` to a separate output buffer — this
+  is the **authentic** P&L signal used by validation regardless of
+  training/validation mode.
+
+Both training and validation call the same kernel. The only difference is
+the scalar values at the pinned pointers.
+
+### Phase 3 — Deprecate the two old kernels (≤1 day)
+
+Delete `experience_env_step_batch` and `backtest_env_step_batch`. Rewire
+all callers to the unified kernel. Remove any dead utility code that was
+specific to one path.
+
+### Phase 4 — Re-tune adaptive-learning mechanisms (≤2 days)
+
+The distillation, collapse-prevention, and health-EMA machinery was tuned
+against the old training reward magnitudes. With shaping removed or
+re-scaled, the health-signal thresholds (`SNAPSHOT_HEALTH_THRESHOLD`,
+`DISTILL_HEALTH_THRESHOLD`, etc.) may need recalibration. The E1 smoke
+test is the canary.
+
+### Phase 5 — Verify on L40S full-scale run (≤1 day)
+
+Production 50-epoch run. Expected signatures of success:
+
+- Training `Sharpe_raw` ≥ 0 by epoch 10; ≥ +0.03 by epoch 30
+- Validation `Sharpe_raw` ≥ training `Sharpe_raw` − 0.05
+- Validation `MaxDD` ≤ 3× training `MaxDD`
+- Validation `WinRate` tracks training `WinRate` ± 5 percentage points
+
+If any of these fail, the analysis is wrong and we revisit Phase 1.
+
+## Risks
+
+1. **Collapse-prevention regresses.** The distillation mechanism was tuned
+   with the existing shaping weights in the loop. Pure-P&L training may
+   cause the network to find attractor states the current mechanisms don't
+   cover. Mitigation: Phase 4 recalibration, E1 smoke test as the gate.
+2. **Training becomes too noisy.** Sparse P&L reward may have insufficient
+   gradient signal for the Q-network to learn. Mitigation: the counterfactual
+   curriculum (N4) should provide dense gradient signal even with pure P&L
+   reward — verify this empirically.
+3. **Historical reward-shaping decisions are load-bearing in ways we don't
+   see.** Some terms may have been added to fix subtle numerical instability
+   (e.g., `reward_noise_scale = 0.05` — label smoothing proxy). Mitigation:
+   Phase 1's per-term inventory must include the provenance commit, not just
+   the semantic purpose.
+
+## Out of scope
+
+- Changing the underlying state representation (96-dim layout, OFI
+  integration, MTF).
+- Changing the network architecture (4-branch factored DQN, ensemble heads,
+  IQN/C51).
+- Re-tuning transaction cost parameters (`tx_cost_bps`, `spread_cost`).
+  These are calibrated to actual broker execution and should remain as-is.
+
+## Success criteria
+
+A pull request that:
+
+1. Deletes `experience_env_step` and `backtest_env_step` kernels in favor
+   of a single unified env step.
+2. Deletes the non-P&L-aligned shaping terms (from Phase 1 inventory).
+3. Passes the E1 collapse-prevention smoke test with final
+   `epoch_q_gap > 0.05`.
+4. Shows validation `Sharpe_raw` within 0.05 of training `Sharpe_raw` by
+   epoch 30 on the L40S 50-epoch production run.
+5. Documents the reward semantics in a single comment block at the
+   unified kernel site.
+
+## References
+
+- Current distillation root-cause analysis: commit `24f96ab78`
+- Smoke test run showing divergence: 2026-04-20 E1 trajectory
+- Reward shaping ablation result: 30% gap closure from disabling 8 credit
+  weights
+- Existing hint at the problem: `experience_kernels.cu:1418` comment
+  *"Regime-adaptive scaling removed to eliminate train/eval mismatch"*