fix(rl): B-6 — ISV-driven adaptive asymmetric Wiener-α (Bayesian shrinkage)
B-5 (asymmetric α with static α_slow=0.001) revealed the static parameter
problem: provably bounds cascades (avg_win peak $2k vs B-4's $40k) BUT
over-conservative in train (dckcc step 800: avg_l > avg_w → Kelly says
don't trade → model can't discover edges; wr_ema crashed to 0.145).
The fundamental tension: static α_slow can't satisfy BOTH
- Train convergence: asymmetry must FADE so model learns from real data
- Boundary safety: asymmetry must ENGAGE at every fold to prevent cascade
B-6 RESOLVES this via Bayesian shrinkage:
trust(n) = min(1, cum_dones / n_full_threshold) [Phase 1]
stability = exp(-CV × cv_gain) [Phase 2]
trust_eff = trust(n) × stability
α_slow_eff = α_slow_min + (α_fast − α_slow_min) × trust_eff
Phase 1 (data-quantity): trust grows with cum_dones; reset_session_state
zeroes cum_dones → asymmetry RESUMES at every boundary. Math: at n=0
α_slow_eff = α_slow_min = 0.001 (full skepticism). At n=n_full = 30k
trades: α_slow_eff = α_fast = 0.05 (full standard Wiener).
Phase 2 (data-quality): Welford CV of reward magnitude gates trust. Stable
signal (CV→0): stability=1, trust opens normally. Volatile signal (CV high):
stability→0, asymmetry persists. cv_gain=0 disables Phase 2.
Per-EMA asymmetry direction encodes Kelly safety semantics:
avg_win: slow-up (skeptical of wins), fast-down
avg_loss: fast-up (admit losses), slow-down (slow forget)
wr_ema: slow-up (skeptical of high WR), fast-down
ISV slots (all signal-derived from cum_dones + Welford):
721 RL_EMA_ALPHA_SLOW_MIN_INDEX = 0.001
722 RL_EMA_TRUST_FULL_THRESHOLD_INDEX = 30000
723 RL_EMA_CV_GAIN_INDEX = 1.0
Threshold calibration (n_full=30k):
- Train: ~1000 cluster steps for trust to fully open → asymmetry active
during cold-start (first 30 steps, cascade prevention) then fades.
- Eval: 30 dones/step × 500 eval steps = 15k dones → trust climbs to 0.5
by eval end → partial protection throughout eval.
Composes:
- B-3 Kelly fractional-trust (Kelly SIZING gated by cum_dones)
- B-6 EMA asymmetric-α (Kelly INPUTS biased conservative by cum_dones)
Both fade as data accumulates; both reset at boundary.
Spec: docs/superpowers/specs/2026-06-01-ema-asymmetric-trust-with-cv-gain.md
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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