These were documented as known limitations in the MEDIUM/LOW pass; now fixed.
1. reward_hack EMA smoothing window — smooth_signal("ema") folded only
window[-1], so reward_hack_smoothing_window had no effect. Now a windowed
EMA folds alpha over the whole retained window (oldest→newest) then the new
sample, so a larger window incorporates more history; a 1-element window
reduces to the old 2-tap form. Updated test_v07126 (0.3 → 0.275).
2. hardware_fit OOM gate wired into `soup train` — the analytical VRAM
predictor was never called despite its docstring. Added
_build_hardware_fit_input (SoupConfig → HardwareFitInput, best-effort;
None when not statically predictable, e.g. batch_size="auto") and
_hardware_fit_preflight, run after device detection. Refuses on predicted
OOM (peak × 1.1 > available) unless the documented --allow-oom-attempt
opt-out is passed; skips silently on CPU / unknown VRAM, and the flag is
threaded through the --gpus re-exec.
3. MoD real token-dropping — mod_forward ran the full block on ALL tokens then
masked (zero compute savings). Now the top-k tokens are gathered into a
shorter sub-sequence, the block runs on ONLY those tokens (real saving),
the gated result is scattered back, and unselected tokens pass through
unchanged. Positional inputs (RoPE cos/sin, 4D-causal attention_mask,
position_ids, cache_position) are gathered to the sub-sequence; any
unsafe-to-gather case (positional forward args, KV cache, non-4D mask)
falls back to the prior correct blend so attention can never be silently
mis-computed. Validated on CPU (gather/scatter/passthrough/savings +
fallback); the sub-sequence-attention numerics still warrant GPU validation
at scale.
Adds tests/test_code_review_deferred.py (7 tests). ruff clean; full suite
14867 passed / 120 skipped.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Alpamys