feat(B4/G5): adaptive gradient budget — IQN/CQL/C51 scale with health & regime
IQN and CQL SAXPY contributions into grad_buf are now scaled by adaptive
budget factors derived from learning_health (ISV[12]) and regime_stability
(ISV[11]):
iqn_budget = 0.10 + 0.30 × health (0.10..0.40)
cql_budget = 0.10 × (1−regime) × health (0 at collapse, volatile+healthy → 0.10)
ens_budget = 0.05 (constant)
c51_budget = 1 − iqn − cql − ens (C51 absorbs headroom at collapse → 0.85)
At collapse (health=0): IQN backed off to 10%, CQL off, C51 takes 85% —
stable directional learning when distributional components are unreliable.
Changes:
- GpuDqnTrainer: add 4 last_*_budget_eff fields (initialized to health=1 defaults)
- GpuDqnTrainer: add read_isv_health_and_regime() public accessor
- apply_iqn_trunk_gradient(): add iqn_budget param; scale = iqn_lambda × readiness × iqn_budget
- apply_cql_saxpy(): add cql_budget param; SAXPY alpha = cql_budget (was 1.0)
- FusedTrainingCtx: add compute_adaptive_budgets() — reads ISV, computes all 4, caches to trainer
- FusedTrainingCtx: add last_{iqn,cql,c51,ens}_budget_eff() accessors
- submit_aux_ops(): call compute_adaptive_budgets() once per step, thread to IQN/CQL sites
- training_loop.rs: B4/G5 propagation block for HEALTH_DIAG logging
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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