mlsanityData to 7 Oct 2026

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

arXiv 2608.01589 ↗First posted 3 Aug 2026cs.AIcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Aug 2026.

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  1. 2026Rethinking Privileged Information in On-Policy Self-Distillation
  2. 2026Purified OPSD: On-Policy Self-Distillation Without Losing How to Think
  3. 2026Beyond Absolute Imitation: Anchored Residual Guidance for Privileged On-Policy Distillation
  4. 2026Learning from a Thoughtful Teacher: Adaptive On-Policy Self-Distillation for Mathematical Reasoning
  5. 2026Rethinking On-Policy Self-Distillation for Thinking Models
  6. 2026Diagnosing On-Policy Self-Distillation for Reasoning Language Models
  7. 2026Overcoming Scaling Limits in On-Policy Self-Distillation for LLM Reasoning
  8. 2026Matching Supervision to the Student's Learning Capacity: A Unified Framework for On-Policy Self-Distillation