On-Policy Distillation
PulseAugur coverage of On-Policy Distillation — every cluster mentioning On-Policy Distillation across labs, papers, and developer communities, ranked by signal.
3 天有情绪数据
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新方法增强了用于 LLM 训练的 on-policy distillation
研究人员开发了改进 on-policy distillation (OPD) 的新方法，OPD 是一种利用大型模型训练小型语言模型的技术。一种方法 TIP，通过分析学生熵和师生分歧来识别信息性 token，实现了显著的内存减少和性能提升。另一种方法 SimCT，通过扩展监督空间以包含多 token 续写来解决不同分词器的问题，恢复了丢失的信号并提高了推理和代码生成任务的性能。此外，EffOPD 通过优化更新轨迹和模块分配来加速 OPD…
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ProteinOPD framework enhances protein design alignment with 8x speedup
Researchers have developed ProteinOPD, a new framework for aligning protein language models (PLMs) with desired functions. This method adapts pretrained PLMs into specialized teachers and distills their knowledge into a…
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New methods enhance on-policy distillation for LLMs
Researchers have developed new methods to improve the efficiency and stability of on-policy distillation (OPD) for large language models. One approach, vOPD, uses a control variate baseline derived from the reverse KL d…
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Researchers refine on-policy distillation for more stable LLM training
Researchers have identified significant empirical failure modes in on-policy distillation (OPD), a technique used for post-training large language models. The standard implementation, which relies on sampled-token log-r…