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Rubrics to Tokens: Bridging Response-level Rubrics and Token-level Rewards in Instruction Following Tasks
arXiv – CS AI|Tianze Xu, Yanzhao Zheng, Pengrui Lu, Lyumanshan Ye, Yong Wu, Zhentao Zhang, Yuanqiang Yu, Chao Ma, Jihuai Zhu, Pengfei Liu, Baohua Dong, Hangcheng Zhu, Ruohui Huang, Gang Yu|
🤖AI Summary
Researchers propose Rubrics to Tokens (RTT), a novel reinforcement learning framework that improves Large Language Model alignment by bridging response-level and token-level rewards. The method addresses reward sparsity and ambiguity issues in instruction-following tasks through fine-grained credit assignment and demonstrates superior performance across different models.
Key Takeaways
- →RTT framework bridges coarse response-level scores with fine-grained token-level credit assignment for better LLM alignment.
- →Token-Level Relevance Discriminator predicts which specific tokens are responsible for constraint satisfaction.
- →RTT-GRPO integrates response-level and token-level advantages in a unified optimization framework.
- →Intra-sample Token Group Normalization method addresses the transition from one-dimensional to three-dimensional reward space.
- →Experimental results show consistent improvements in both instruction-level and rubric-level accuracy across different models.
#reinforcement-learning#large-language-models#llm-alignment#token-level-rewards#instruction-following#rubric-based-rl#model-optimization#ai-training
Read Original →via arXiv – CS AI
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