In the news
Sequential Beats Joint: On the Interplay between On-Policy Distillation and RLVR
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- Researchers show that training language models with on-policy distillation first, then reinforcement learning with verifiable rewards, outperforms joint optimization on reasoning tasks.
- Why it matters
- Engineers building post-training pipelines for reasoning models should consider this sequential approach instead of combining both signals in a single training step.
- Watch out
- 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
The patterns behind this
- RL from Verifiable Rewards (RLVR)
- Sequential Pipeline Agents
- Reinforcement Learning from Human Feedback
Each one covers how the technique works, when it earns its cost, and where it breaks.
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