New metrics predict effectiveness of privileged information in LLM self-distillation

Researchers have developed new methods to predict the effectiveness of privileged information (PI) in on-policy self-distillation (OPSD) for large language models (LLMs). The study found that the content and source of PI significantly impact training gains, with more information-dense trajectories and PI from more capable generators yielding better results. Two KL-based metrics, sampled-token reverse KL and error-step KL contrast, were found to be more consistent predictors of post-training performance than direct utility estimates, offering practical guidance for constructing and evaluating PI in OPSD. AI

IMPACT Provides practical guidance for improving LLM training efficiency and effectiveness through better utilization of privileged information.

RANK_REASON The cluster contains an academic paper detailing a new method for training large language models. [lever_c_demoted from research: ic=1 ai=1.0]

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• Error-step KL contrast
• Grpo
• KL-based metrics
• large-language models
• On-policy self-distillation
• Privileged Information
• Sampled-token reverse KL

• paper
• model release

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