新闻
Coupled Calibration and Learning: Mitigating Teacher Bias in LLM Distillation without Target-Domain Reward Feedback
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers propose Coupled Calibration and Learning, an algorithm that reduces teacher bias in LLM distillation using only source-domain reward feedback.
- 为何重要
- Matters when training smaller models from larger ones where teacher errors could propagate and target-domain feedback is unavailable or expensive.
- 注意
- Algorithm proven theoretically but paper is recent arXiv submission without reported empirical validation on real distillation tasks.
- llm
- language model
- distill
- token
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- Reinforcement Learning from Human Feedback
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- RL from Verifiable Rewards (RLVR)
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