lsft-code-cruxreal-l1b

Latent-SFT-code — latent reasoning on code execution, real public data

Latent-reasoning checkpoint for code output prediction (execution reasoning), trained with Latent-SFT on a three-tier difficulty ladder built entirely from real public datasets. No synthetic data.

Status. Training is complete (all 8 pipeline steps). The four-criteria latent verification (latent ablation / accuracy / adaptive depth / platform-native signal) has not been run yet — no claim is made here about whether the latent carries content.

Training data

1488 train / 165 val, seed 0. Tier distribution: T0 562 · T1 612 · T2 479. Format: {problem, cot, solution, cot_answer} (answer wrapped in \boxed{}). Same underlying pool as the CoLaR and LT-Tuning runs, so the three platforms are directly comparable.

Difficulty ladder: depth_proxy = #lines + 8 × #loops (CRUXEval/MBPP, median split → T0/T1); LiveCodeBench uses its native numsteps and forms T2. Ordinal scale only.

Scope note on the reasoning chain. Questions and answers are 100% real public data. The intermediate chain is derived mechanically from the real source code (the function's own body lines, capped at 7, plus one templated concluding sentence) — not human- or model-written CoT.

Recipe

Field-for-field identical to the earlier synthetic-data run's configuration — the only change is the data.

Training results

train_loss: encoder 37.41 · decoder 3.88 · union 0.336 · stage2 10.23.

Local patches applied

The upstream launcher needed three fixes to run to completion here. Recording them so results are reproducible:

The second and third fixes proved themselves on first use: the run stopped 2 minutes into the encoder step with the real error, instead of running all 8 steps and reporting success.