arXiv:2606.17199v1 Announce Type: cross Abstract: Standard on-policy distillation (OPD) for large language models estimates the reverse-KL objective using student-sampled tokens, yielding an unbiased single-sample Monte Carlo estimator that avoids vocabulary-wide computation. However, we show that this estimator suffers from severe training pathologies in practice: sample inefficiency, unstable generation dynamics, and a substantial performance gap compared to exact full-vocabulary OPD. Reward-level diagnosis traces these pathologies to the log-ratio reward, which is unbounded by construction,
The paper addresses current challenges in on-policy distillation for large language models, indicating active research into improving their training stability and efficiency.
Improved OPD techniques could lead to more stable and performant large language models, accelerating their development and deployment across various applications.
The proposed 'bounded power transformation' offers a solution to the instability and inefficiency of existing on-policy distillation methods, potentially making LLM training more robust.
- · AI researchers
- · Large language model developers
- · AI-powered applications
- · Less efficient LLM training methods
More stable and efficient training of large language models becomes possible.
This could lead to faster iteration and deployment of more capable AI systems.
Accelerated LLM development might further fuel the growth of AI agents and complex AI applications.
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Read at arXiv cs.AI