6 papers
Learning to Compress: Unlocking the Potential of Large Language Models for Text Representation
Yeqin Zhang, Yizheng Zhao, Chen Hu +4
Text representation plays a critical role in tasks like clustering, retrieval, and other downstream applications. With the emergence of large language models (LLMs), there is incre…
Random Policy Valuation is Enough for LLM Reasoning with Verifiable Rewards
Haoran He, Yuxiao Ye, Qingpeng Cai +4
RL with Verifiable Rewards (RLVR) has emerged as a promising paradigm for improving the reasoning abilities of large language models (LLMs). Current methods rely primarily on polic…
GitTaskBench: A Benchmark for Code Agents Solving Real-World Tasks Through Code Repository Leveraging
Ziyi Ni, Huacan Wang, Shuo Zhang +15
Beyond scratch coding, exploiting large-scale code repositories (e.g., GitHub) for practical tasks is vital in real-world software development, yet current benchmarks rarely evalua…
SE-Agent: Self-Evolution Trajectory Optimization in Multi-Step Reasoning with LLM-Based Agents
Jiaye Lin, Yifu Guo, Yuzhen Han +11
Large Language Model (LLM)-based agents have recently shown impressive capabilities in complex reasoning and tool use via multi-step interactions with their environments. While the…
DialogueReason: Rule-Based RL Sparks Dialogue Reasoning in LLMs
Yubo Shu, Zhewei Huang, Xin Wu +3
We propose DialogueReason, a reasoning paradigm that uncovers the lost roles in monologue-style reasoning models, aiming to boost diversity and coherency of the reasoning process.…
Unearthing Gems from Stones: Policy Optimization with Negative Sample Augmentation for LLM Reasoning
Zhaohui Yang, Yuxiao Ye, Shilei Jiang +4
Recent advances in reasoning language models have witnessed a paradigm shift from short to long CoT pattern. Given the substantial computational cost of rollouts in long CoT models…