新闻
Understanding Reasoning from Pretraining to Post-Training
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers studied how pretraining choices affect reinforcement learning performance in language models using chess and math as controlled testbeds.
- 为何重要
- Engineers optimizing LLM training pipelines need to understand how pretraining investments translate to post-training RL gains on reasoning tasks.
- 注意
- Findings use chess and math domains as proxies; generalization to broader reasoning tasks and real-world LLM scales remains unclear.
- llm
- language model
- reasoning
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- Reinforcement Learning from Human Feedback
- MMAU: Massive Multitask Agent Understanding
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