新闻
Sequential Beats Joint: On the Interplay between On-Policy Distillation and RLVR
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers show that training language models with on-policy distillation first, then reinforcement learning with verifiable rewards, outperforms joint optimization on reasoning tasks.
- 为何重要
- Engineers building post-training pipelines for reasoning models should consider this sequential approach instead of combining both signals in a single training step.
- 注意
- The paper tests only logic and math reasoning benchmarks; effectiveness on other domains and the practical overhead of two-stage training remain unclear.
- llm
- reasoning
- post-train
- distill
- token
这条新闻背后的模式
- RL from Verifiable Rewards (RLVR)
- Sequential Pipeline Agents
- Reinforcement Learning from Human Feedback
每个模式都讲清楚技术如何运作、何时值得投入，以及在哪里会失效。
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