12 papers
Bridging Auxiliary Constraints to Resolve Instruction Following in Large Reasoning Models
Zhengyi Zhao, Shubo Zhang, Huimin Wang +7
Large Reasoning Models (LRMs) have demonstrated impressive capabilities in many tasks, yet they struggle with reliably following multiple instructions, either by failing to satisfy…
Beyond the Literal: Decomposing Pragmatic Intent in Multimodal Meme Understanding
Zhengyi Zhao, Shubo Zhang, Zezhong Wang +6
When asked what a meme or sarcastic post means, Large Vision Language Models (LVLMs) tend to describe what the image shows rather than what the author is trying to communicate. Sta…
CodeScaler: Scaling Code LLM Training and Test-Time Inference via Reward Models
Xiao Zhu, Xinyu Zhou, Boyu Zhu +5
Reinforcement Learning from Verifiable Rewards (RLVR) has driven recent progress in code large language models by leveraging execution-based feedback from unit tests, but its scala…
Guaranteeing Knowledge Integration with Joint Decoding for Retrieval-Augmented Generation
Zhengyi Zhao, Shubo Zhang, Zezhong Wang +7
Retrieval-Augmented Generation (RAG) significantly enhances Large Language Models (LLMs) by providing access to external knowledge. However, current research primarily focuses on r…
Balancing the Reasoning Load: Difficulty-Differentiated Policy Optimization with Length Redistribution for Efficient and Robust Reinforcement Learning
Yinan Xia, Haotian Zhang, Huiming Wang
Large Reasoning Models (LRMs) have shown exceptional reasoning capabilities, but they also suffer from the issue of overthinking, often generating excessively long and redundant an…
From the Inside Out: Progressive Distribution Refinement for Confidence Calibration
Xizhong Yang, Yinan Xia, Huiming Wang +1
Leveraging the model's internal information as the self-reward signal in Reinforcement Learning (RL) has received extensive attention due to its label-free nature. While prior work…