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This research introduces Problem-Space-Guided OPSD, which enhances self-distilled reasoning by replacing standard solution traces with trajectory-grounded guidance focused on problem constraints and state transitions. Experimental results across multiple model scales demonstrate that this structural approach yields superior question-only accuracy compared to methods relying on reference-specific token targets. The findings underscore the critical importance of how privileged information is represented during training for improving generalizable mathematical reasoning capabilities.
Is More Privileged Information Better? From Solution Traces to Problem-Solving Structure in Self-Distilled Reasoning
arXiv:2608.01589v1 Announce Type: new 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.
Title:Is More Privileged Information Better? From Solution Traces to Problem-Solving Structure in Self-Distilled Reasoning
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.
Artificial Intelligence (cs.AI)
arXiv:2608.01589 [cs.AI]
(or arXiv:2608.01589v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.01589 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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