ExOPD λ=1.0 + KL-to-reference trust region, step 10/15
From a smoke test of a fix for a reasoning-shortcut reward hack found in
On-Policy Distillation with Reward Extrapolation
(ExOPD) training. Full context:
ntlfi/opd_training (see
SESSION_LOG.md and algos/exopd_incident_2026-08-07.md).
GSM8K test accuracy: 79.53% (1049/1319) — essentially matches the untrained base model (79.61%), and a large improvement over the same training run without this fix (74.15% at a comparable stage). Training-time metrics: loss=0.218, kl_penalty=0.0024.
Teacher: meta-llama/Meta-Llama-3-70B-Instruct (frozen). Student/base:
meta-llama/Meta-Llama-3-8B-Instruct. λ=1.0 (standard on-policy distillation
reduction, no reward-extrapolation reference term active), plus an added
KL(π_θ‖π_ref) trust-region penalty (kl_ref_coef=0.1, not part of the
published method — added to counter a reward-hacking failure mode this
repo found independently) using Schulman's k3 estimator.
For comparison, plain OPD (this repo's closed-form top-K reverse-KL loss, same teacher/student) reaches 81.35% at its best checkpoint.
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