Is More Privileged Information Better? From Solution Traces to Problem-Solving Structure in Self-Distilled Reasoning
Not on the map. Why?
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.
Builds on
0 of 0 references.
No references here yet.
Built on by
Citing papers per year: 2026: 7.
7 papers.
- 2026PAST: Privileged Adaptation from Complete Student Trajectories for On-Policy Self-Distillationcited by 4
- 2026What Should a Self-Teacher See? Privileged Context Design for On-Policy Self-Distillationcited by 2
- 2026SCOPE-OPSD: Fisher-Conditioned Privileged Subspaces for On-Policy Self-Distillationcited by 1
- 2026What Does Privileged Information Add to On-Policy Self-Distillation?cited by 1
- 2026When Clipping Reverses Correction: Failure Dynamics of Pointwise Forward-KL On-Policy Self-Distillationcited by 0
- 2026PG-SFT: Balancing Capability Acquisition and Retention in Offline Agent Fine-Tuningcited by 0
- 2026SpatialOPSD: Self-Distilling Spatial Intelligence from Verified Coding Agent Tracescited by 0
- 2026SpatialOPSD: Self-Distilling Spatial Intelligence from Verified Coding Agent Traces8 Oct 2026
- 2026PG-SFT: Balancing Capability Acquisition and Retention in Offline Agent Fine-Tuning1 Oct 2026
- 2026When Clipping Reverses Correction: Failure Dynamics of Pointwise Forward-KL On-Policy Self-Distillation30 Sep 2026
- 2026What Should a Self-Teacher See? Privileged Context Design for On-Policy Self-Distillation22 Sep 2026
- 2026What Does Privileged Information Add to On-Policy Self-Distillation?17 Sep 2026
- 2026SCOPE-OPSD: Fisher-Conditioned Privileged Subspaces for On-Policy Self-Distillation11 Sep 2026
- 2026PAST: Privileged Adaptation from Complete Student Trajectories for On-Policy Self-Distillation9 Aug 2026
Similar, not cited
Close in wording, no citation either way
- 2026Rethinking Privileged Information in On-Policy Self-Distillation
- 2026Purified OPSD: On-Policy Self-Distillation Without Losing How to Think
- 2026Beyond Absolute Imitation: Anchored Residual Guidance for Privileged On-Policy Distillation
- 2026Learning from a Thoughtful Teacher: Adaptive On-Policy Self-Distillation for Mathematical Reasoning
- 2026Rethinking On-Policy Self-Distillation for Thinking Models
- 2026Diagnosing On-Policy Self-Distillation for Reasoning Language Models
- 2026Overcoming Scaling Limits in On-Policy Self-Distillation for LLM Reasoning
- 2026Matching Supervision to the Student's Learning Capacity: A Unified Framework for On-Policy Self-Distillation