fix(rl): mean_abs_pnl_ema tracks all non-zero rewards, not just closes

Cluster smoke `alpha-rl-9cbpj` diag.jsonl revealed that 64% of
non-zero reward events (170 of 266 across 1000 steps) occur on
non-done steps — mid-trade PnL deltas from trail-stop adjustments,
mark-to-market, or partial fills. The reward_scale controller's
mean_abs_pnl_ema was done-gated, so these mid-trade reward
magnitudes never contributed to the scale calibration.

Concretely: closed-trade |PnL| settled around $832-910, controller
calibrated `scale = 1/910 ≈ 0.0011`. Mid-trade swings can reach
$2,390 (step 590 raw reward); scaled by 0.0011 they produce
reward = -$58.8, which V regression must learn to predict. With
the C51 V-head atom support of [−1, +1] the −58.8 target generates
MSE ≈ 3,456 (canonical incident, prior smoke). The controller is
calibrated for the wrong distribution.

## Fix

The `ema_update_on_done` kernel's "dones" parameter is really a
generic gate tested as `d >= 0.5f`. Passing `reward_abs_d` as the
gate (instead of `dones_d`) gives "gate on |reward| ≥ 0.5" which
for any practical dollar magnitude means "gate on non-zero reward
event". Zero-reward steps still stay excluded so the EMA isn't
biased toward zero on idle hold steps.

One-line change at the launch site (the `obs` and gate arguments
become the same buffer, `reward_abs_d`). No kernel modification
needed — the same kernel serves both done-gated EMAs (e.g.
`mean_trade_duration`) and reward-event-gated EMAs (this one)
just by choice of which buffer is passed as the gate.

## Expected effect on next smoke

The new mean_abs_pnl_ema will track the average |reward| across
both close events ($832-910) and mid-trade events ($100-2400).
With mid-trade magnitudes typically 2-3× larger than close
magnitudes, the new mean will be higher → reward_scale lower →
all reward magnitudes (close AND mid-trade) get squeezed into
the V-head's atom support more consistently.

The step-590 spike (l_v=3,456) should drop to O(1).

## Verified gates (local sm_86)

All R-phase tests still green — the change is a single-argument
swap at the launch site, no kernel logic touched. Tests pass
unchanged because they don't exercise the mid-trade reward path
(test harness uses synthetic LobSim with simple cross-and-close
mechanics, not the trail-stop / partial-fill mid-trade dynamics
that surfaced in the cluster smoke).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

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