diff --git a/crates/ml-alpha/src/trainer/integrated.rs b/crates/ml-alpha/src/trainer/integrated.rs index 2b335d02c..1043884da 100644 --- a/crates/ml-alpha/src/trainer/integrated.rs +++ b/crates/ml-alpha/src/trainer/integrated.rs @@ -2379,9 +2379,27 @@ impl IntegratedTrainer { } } - // Update ISV[423] MEAN_ABS_PNL_EMA from |reward| over closed - // trades (dones_d gating). R5 controllers consume this on - // their next per-step launch below. + // Update ISV[423] MEAN_ABS_PNL_EMA from |reward| over ALL + // non-zero reward events (NOT just trade closes). + // + // Originally gated on dones_d so only closed-trade realized + // PnL contributed to the EMA. Cluster smoke `alpha-rl-9cbpj` + // diag revealed that 170 of 266 non-zero reward events occur + // on non-done steps (mid-trade PnL deltas from trail-stop + // adjustments / mark-to-market / partial fills). These + // mid-trade swings can be 2-3× larger than realized close + // PnL — invisible to a done-gated EMA, they end up scaled + // by `reward_scale = 1/mean_abs_pnl_close` which is calibrated + // for the smaller magnitude. Result: V regression target + // spikes ×50-100 on volatile mid-trade steps (l_v=3,456 + // observed at step 590 with raw $2,390 PnL × scale=0.0011). + // + // The kernel's "done" parameter is really a generic gate + // tested as `d >= 0.5`. Passing reward_abs_d as the gate + // gives "gate on |reward| ≥ 0.5" (= "non-zero reward" for + // any practical magnitude in dollars) — exactly the + // semantics we want. Zero-reward steps stay excluded so the + // EMA isn't biased toward zero on idle steps. { let cfg = LaunchConfig { grid_dim: (1, 1, 1), @@ -2396,7 +2414,7 @@ impl IntegratedTrainer { .arg(&slot_i) .arg(&alpha) .arg(&self.reward_abs_d) - .arg(&self.dones_d) + .arg(&self.reward_abs_d) // gate on |reward|>0, not just on dones .arg(&b_size_i); unsafe { launch