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Off-Context GRPO: Learning to Reason on Hard Problems using Privileged Information
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Off-Context GRPO uses privileged training guidance like solution prefixes to help language models learn reasoning on hard math problems where standard reinforcement learning provides no learning signal.
- 为何重要
- Relevant for engineers training reasoning models on difficult problems where models struggle to generate any correct solutions during standard reinforcement learning.
- 注意
- The method requires importance correction to avoid training-deployment mismatch when using guided rollouts. Real-world applicability beyond mathematical benchmarks remains undemonstrated.
收听本摘要
- language model
- reasoning
- prompt
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