Predicting Privileged Information Effectiveness for On-Policy Self-Distillation
On-policy self-distillation (OPSD) enables large language models (LLMs) to learn from privileged information (PI) without a separate teacher model.
ProofPaper ↗
Key points
- We systematically evaluate 16 PI configurations across three multi-turn agentic benchmarks and three student models that vary in scale and reasoning mode.
- We find that PI effectiveness depends on its content, generation source, and the student model.
- To assess PI before training, we compare PI utility with two KL-based metrics: sampled-token reverse KL ($D$) and error-step KL contrast ($Δd^{-}{err}$).
- Empirically, selecting each task's PI by $Δd^{-}{err}$ also improves post-training performance over random selection, most when the PI candidates differ widely.
Sources (2)
- [1]Predicting Privileged Information Effectiveness for On-Policy Self-DistillationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 06:05 PM
On-policy self-distillation (OPSD) enables large language models (LLMs) to learn from privileged information (PI) without a separate teacher model.
We systematically evaluate 16 PI configurations across three multi-turn agentic benchmarks and three student models that vary in scale and reasoning mode.
- [2]Predicting Privileged Information Effectiveness for On-Policy Self-DistillationHugging Face Daily Papers · Oct 15, 12:00 AM · same content
Extractive summary: sentences quoted from the sources.
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