Single-sequence KV-cache decode (xtrain-model/src/decode.rs): per-layer K/V
cache + single-token incremental forward (prefill = first prompt.len() decode
steps, one code path). Mirrors model::block_forward at the raw-Tensor level (no
autograd tape — inference needs no grads), using rope_at + decode_attention.
Cache is host-accumulated token-major f32, rebuilt per step (the honest M2a
baseline; M2b moves it device-side + batched ragged).
Gate (the M2 centerpiece): KV-cache greedy decode is TOKEN-IDENTICAL to the
naive full-recompute greedy — tests/decode_kv.rs (small GQA model, F32, 24
tokens) and corroborated on the v12 1.05B SFT checkpoint (cached eval =
naive eval byte-for-byte: format 100/100, correct 8/100).
eval_arith --cached A/Bs the two paths + reports decode tok/s. Measured on v12
(1.05B, batch 1, F32): the cache win is sequence-length-dependent —
max_new=32 naive 108 vs cached 111 tok/s (~1.0x; overhead-bound)
max_new=128 naive 69 vs cached 133 tok/s (~1.9x)
max_new=256 naive OOM vs cached 129 tok/s
Cached throughput stays ~constant (O(1)/token) while naive decays (O(t)/token,
O(seq^2) graph → OOM at length). Short eval prompts are overhead-bound, so the
cache matters for long rollouts (DPO/GRPO), not the arithmetic eval itself.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
eval_arith: load ckpt, greedy-generate per held-out prompt, parse \boxed{}
via the shared task checker, report format(boxed) + correctness pass-rates.
Reused as the verifiable-eval harness for M3 (DPO) / M4 (GRPO).
M1 result (100 held-out prompts, v12 1.05B base): SFT moves answer-format
adherence 0% -> 100%, arithmetic correctness 8% -- the intended split (SFT
buys the format; correctness is the verifiable-reward job of M3/M4). Logged
in docs/18 implementation log + a Phase-3 row in docs/evolution.md.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
