5 papers · 1 filter
Efficient and Trainable Language Model Test-Time Scaling via Local Branch Routing
Yutong Yin, Mingyu Jin, Jin Pan +12
Test-time scaling improves language-model reasoning, but existing approaches often face a difficult trade-off: long chain-of-thought sampling remains single-threaded, while sentenc…
All Circuits Lead to Rome: Rethinking Functional Anisotropy in Circuit and Sheaf Discovery for LLMs
Xi Chen, Mingyu Jin, Jingcheng Niu +7
In this paper, we present empirical and theoretical evidence against a central but largely implicit assumption in circuit and sheaf discovery (CSD), which we term the Functional An…
Fin-Bias: Comprehensive Evaluation for LLM Decision-Making under human bias in Finance Domain
Xiaoyu Hu, Jinman Zhao
Large language models (LLMs) are increasingly deployed in financial contexts, raising critical concerns about reliability, alignment, and susceptibility to adversarial manipulation…
EndPrompt: Efficient Long-Context Extension via Terminal Anchoring
Han Tian, Luxuan Chen, Xinran Chen +10
Extending the context window of large language models typically requires training on sequences at the target length, incurring quadratic memory and computational costs that make lo…
-GRPO: Unifying the GRPO Frameworks with Learnable Token Preferences
Yining Wang, Jinman Zhao, Chuangxin Zhao +3
Reinforcement Learning with Human Feedback (RLHF) has been the dominant approach for improving the reasoning capabilities of Large Language Models (LLMs). Recently, Reinforcement L…