reinforcement-fine-tuning

SignRAG 提出一个统一框架，结合分层预训练、目标域检索增强与检索效用引导的强化微调（RUG-RFT），将 decoder-only LLM 用于无词汇（gloss-free）手语翻译，在多个基准上达到 SOTA，并首次在 CSL-Daily 上全面超越有词汇监督的方法。

Introduces ReliableTableQA, a framework for training LLMs to annotate statistical reliability of tabular QA results, showing that a small SFT set is sufficient and GRPO only helps when SFT is under-trained.

This paper introduces Retention-aware Policy Optimization (RaPO) to mitigate catastrophic forgetting in visual continual learning using reinforcement fine-tuning. RaPO uses trajectory-level reward shaping and cross-task advantage normalization to close the gap between reinforcement and supervised fine-tuning in class- and domain-incremental learning.