First post-training milestone (docs/18). Lands the verifiable task + its data
pipeline, all verified host-side (no CUDA); the SFT run itself reuses the
existing --sft-tsv path on the GPU box.
- task.rs: the shared task spec — two-operand integer arithmetic, answer in
\boxed{N}, with parse_boxed_answer + check_answer (exact-match rule-based
reward). One module reused by M1 (SFT data), M3 (DPO pairs), M4 (GRPO reward).
- gen_arith_task bin: writes arith_sft.tsv (--sft-tsv format) + held-out
arith_eval_prompts.txt (greedy_sample format) + arith_eval_gold.txt; train
deduped, eval disjoint from train.
- data.rs: extract assistant-only masking into a pure, testable sft_row()
(behavior-preserving; single-turn bit-identical to fbf4ac2).
Gate (verified locally, no_cuda): cargo test -p xtrain-train --lib = 9/9 pass
(masking, SFT-target self-consistency over 2000 samples, parser edges, seed
determinism); a 200/50 gen run = clean 2-col TSV, correct gold incl. negatives,
0 train/eval leakage. SFT training run + format-eval pending on dash5.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Design doc for a from-scratch post-training infra on top of xtrain. Ladder:
SFT (have it) → DPO → reward model (optional) → GRPO, each rung one new
post-training systems concept + a hard correctness gate (grad-check, PyTorch
parity, degenerate checks, a falsifiable 'it learns' signal).
Decisions aligned with the user (D1-D4):
- D1 scope: DPO → GRPO, reward model optional.
- D2 reward: rule-based / verifiable first; learned RM deferred.
- D3 rollout: build the KV-cache incremental-decode engine UP FRONT (not
naive-first) as the foundational milestone before DPO/GRPO.
- D4 task: a verifiable task (arithmetic/format) with deterministic exact-match
reward, for a clean RL signal.
Locked milestone order: M1 SFT task baseline → M2 KV-cache decode engine
(token-identical gate) → M3 DPO → M4 GRPO → M5 optional reward model. Status:
design only, no implementation yet.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
