7 papers
Bridging Interleaved Multi-Modal Reasoning as a Unified Decision Process
Zican Hu, Xuyang Hu, Yiming Liu +10
Unified multi-modal models (UMMs) have shown promising interleaved text-image reasoning capabilities, yet effectively optimizing such multi-turn generation via reinforcement learni…
Reasoning over Boundaries: Enhancing Specification Alignment via Test-time Deliberation
Haoran Zhang, Yafu Li, Xuyang Hu +4
Large language models (LLMs) are increasingly applied in diverse real-world scenarios, each governed by bespoke behavioral and safety specifications (spec) custom-tailored by users…
Achieving Gold-Medal-Level Olympiad Reasoning via Simple and Unified Scaling
Yafu Li, Runzhe Zhan, Haoran Zhang +25
Recent progress in reasoning models has substantially advanced long-horizon mathematical and scientific problem solving, with several systems now reaching gold-medal-level performa…
LEAD: Length-Efficient Adaptive and Dynamic Reasoning for Large Language Models
Songtao Wei, Yi Li, Zhikai Li +7
Large reasoning models, such as OpenAI o1 and DeepSeek-R1, tend to become increasingly verbose as their reasoning capabilities improve. These inflated Chain-of-Thought (CoT) trajec…
Diversity-Incentivized Exploration for Versatile Reasoning
Zican Hu, Shilin Zhang, Yafu Li +7
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a crucial paradigm for incentivizing reasoning capabilities in Large Language Models (LLMs). Due to vast state-…
Test-Time Preference Optimization: On-the-Fly Alignment via Iterative Textual Feedback
Yafu Li, Xuyang Hu, Xiaoye Qu +2
Large language models (LLMs) demonstrate impressive performance but lack the flexibility to adapt to human preferences quickly without retraining. In this work, we introduce Test-t…