fix(rl): sparse-aware EMA + Q→π distillation breaks defensive trap

Two coupled fixes addressing vj5f6 findings:

(1) WIN_clamp oscillation — sparse-aware EMA

  vj5f6 showed WIN_clamp oscillating 1.0 ↔ 67.0 across 40k steps.
  Root cause: the Wiener-α blend in rl_reward_clamp_controller
  treated pos_max=0 as "no win this step ≡ win magnitude is zero,"
  exponentially decaying the EMA toward 0 during dry-spell windows
  (no closed winning trades). With α=0.4, ten dry steps decayed EMA
  by 0.6^10 ≈ 0.006, collapsing WIN back to MIN_WIN=1.0 floor.

  Fix: only update pos_max_ema AND clip_rate_ema AND MARGIN when
  pos_max > 0. A dry step is "no signal," not "zero signal." The
  EMA retains its last winning-period estimate; the controller
  doesn't ratchet on stale data.

(2) Q→π distillation — couples Q's improved calibration to π

  vj5f6 showed l_q dropping 100× (2.37 → 0.02) but reward economics
  IDENTICAL to 8xwq8 (no C51 V_MAX lift). Per Option B, π drives
  action selection but is trained by PPO surrogate using advantage
  = returns - V. V regression doesn't benefit from C51 calibration,
  so Q's improved knowledge stays trapped in the critic head.

  Deep audit revealed a self-reinforcing defensive trap:
    Q learned "big positions lose money" → π_target favors small
    actions → π picks a3+a4 (tiny long / Hold) → position lots ≈ 0
    → rewards mostly 0 → V learns "everything is 0" → V_pred ≈ 0
    → advantage = returns - V_pred ≈ 0 → PPO gradient ≈ 0 → π
    frozen at defensive attractor → loop. Trade count dropped 3×
    (rdgzl 25k → 8xwq8/vj5f6 9k closes per 10k steps), win rate
    inversely correlated with l_q (50% early → 22% late) because
    only forced closes happen (stops = losses).

  Fix: new rl_q_pi_distill_grad.cu computes
    π_target = softmax(E_Q[s,*] / τ)
    ∂L/∂logits[a] = λ × (π_new(a) - π_target(a))
  and ADDS this gradient to pi_grad_logits AFTER the PPO surrogate
  backward. Couples Q's preferences directly into π's update without
  going through advantage. λ=0.01 (small, PPO dominant), τ=1.0
  (canonical Boltzmann). 3 new ISV slots (λ + τ + KL_ema diag).

Diag exposes c51_v_max/v_min, q_distill_lambda/temperature, and
q_distill_kl_ema so the adaptation + distillation loop is observable.

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

crates/ml-alpha/build.rs

@@ -70,6 +70,7 @@ const KERNELS: &[&str] = &[
     "rl_pi_action_kernel",           // audit Option B: π drives action selection via multinomial sampling from softmax(pi_logits); Q becomes pure critic
     "rl_reward_clamp_controller",    // audit 2026-05-24: adaptive [-LOSS, +WIN] clamp from positive-tail EMA; replaces static [-3, +1] that crushed winning-trade signal in rmgm5
     "rl_atom_support_update",        // audit 2026-05-24 followup: refreshes atom_supports_d from ISV V_MIN/V_MAX so C51 atom span adapts with reward clamp (Q learning was capped at V_MAX=1.0)
+    "rl_q_pi_distill_grad",          // audit 2026-05-24 vj5f6 followup: KL(softmax(E_Q/τ) || π_new) gradient ADDED to pi_grad_logits — couples Q's improved C51 calibration to action selection (was decoupled per Option B)
 ];
 
 // Cache bust v31 — five new reduce / derive kernels populate the input