opsd-c54b853f·2 events·first seen Aliases: OPSD
A new arXiv preprint proposes Rubric-Conditioned Self-Distillation (RCSD), a post-training framework that replaces scalar reward signals and noisy chain-of-thought annotations with structured rubrics for fine-grained credit assignment. The method conditions a teacher model on criterion-level rubrics to provide token-level guidance on the student's own sampled trajectories, avoiding reliance on a single reference rationale. Evaluated on science reasoning benchmarks, RCSD outperforms GRPO by 1.0 points and OPSD by 0.9 points on average.
SGSD is a new on-policy self-distillation method for LLM reasoning that replaces trusted privileged information (e.g., reference answers) with an experience-derived skill bank of skill-mistake pairs. It constructs a multi-teacher pool, validates each teacher's contribution via a verifier, and applies a gated objective to distill informative disagreements while suppressing noisy signals. On Qwen3-1.7B, SGSD outperforms GRPO by 6.2% and answer-conditioned OPSD by 1.7% on average across AIME24, AIME25, and HMMT25. The method relaxes the assumption of trusted privileged information, making self-distillation more practical under weaker supervision.