1 citations · 1 across the 5 of their papers we have counts for
6 papers · 1 filter
When to Memorize and When to Stop: Gated Recurrent Memory for Long-Context Reasoning
Leheng Sheng, Yongtao Zhang, Wenchang Ma +6
While reasoning over long context is crucial for various real-world applications, it remains challenging for large language models (LLMs) as they suffer from performance degradatio…
GenEnv: Difficulty-Aligned Co-Evolution Between LLM Agents and Environment Simulators
Jiacheng Guo, Ling Yang, Peter Chen +6
Training capable Large Language Model (LLM) agents is critically bottlenecked by the high cost and static nature of real-world interaction data. We address this by introducing GenE…
Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models
Yinjie Wang, Ling Yang, Bowen Li +3
We propose TraceRL, a trajectory-aware reinforcement learning framework for diffusion language models (DLMs) that incorporates preferred inference trajectory into post-training, an…
ReasonFlux-PRM: Trajectory-Aware PRMs for Long Chain-of-Thought Reasoning in LLMs
Jiaru Zou, Ling Yang, Jingwen Gu +4
Process Reward Models (PRMs) have recently emerged as a powerful framework for supervising intermediate reasoning steps in large language models (LLMs). Previous PRMs are primarily…
Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning
Yinjie Wang, Ling Yang, Ye Tian +2
We propose CURE, a novel reinforcement learning framework with a dedicated reward design that co-evolves coding and unit test generation capabilities based on their interaction out…
AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection
Kai Hua, Steven Wu, Ge Zhang +1
Recently, there has been growing interest in collecting reasoning-intensive pretraining data to improve LLMs' complex reasoning ability. Prior approaches typically rely on supervis…