MEGA Hub

Is More Privileged Information Better? From Solution Traces to Problem-Solving Structure in Self-Distilled Reasoning

Authors

Do you know Xuyang Zhao?You can claim authorship or link another user.Do you know Liting Zhang?You can claim authorship or link another user.Do you know Zichen Xu?You can claim authorship or link another user.Do you know Zhihu Wang?You can claim authorship or link another user.Do you know Xu Caiyue?You can claim authorship or link another user.Do you know Shiwan Zhao?You can claim authorship or link another user.Do you know Qicheng Li?You can claim authorship or link another user.

Abstract

On-policy self-distillation (OPSD) improves reasoning by using a privileged view of a model conditioned on reference solutions to supervise a student view that observes only the question. However, the teacher-provided token-level targets may depend on reference-specific information unavailable at inference time. We propose Problem-Space-Guided OPSD (PS-OPSD), which replaces the complete solution with trajectory-grounded guidance describing the initial state, goal conditions, constraints, and a selected state-transition path. The student rollout and OPSD objective remain unchanged. Across three mathematical reasoning benchmarks and model scales ranging from 1.7B to 8B, PS-OPSD achieves the highest aggregate question-only accuracy among the compared methods. Controlled experiments further indicate that guidance relevance and path coherence contribute to these gains, highlighting the representation of privileged information as an important design choice in OPSD.

Community

00