1 citations · 2 across the 7 of their papers we have counts for
6 papers · 1 filter
Critique-RL: Training Language Models for Critiquing through Two-Stage Reinforcement Learning
Zhiheng Xi, Jixuan Huang, Xin Guo +15
Training critiquing language models to assess and provide feedback on model outputs is a promising way to improve LLMs for complex reasoning tasks. However, existing approaches typ…
Scaling Long-Horizon LLM Agent via Context-Folding
Weiwei Sun, Miao Lu, Zhan Ling +4
Large language model (LLM) agents are fundamentally constrained by context length on long-horizon tasks. We introduce Context-Folding, a framework that empowers agents to actively…
Scaling LLM Multi-turn RL with End-to-end Summarization-based Context Management
Miao Lu, Weiwei Sun, Weihua Du +4
We study reinforcement learning (RL) fine-tuning of large language model (LLM) agents for long-horizon multi-turn tool use, where context length quickly becomes a fundamental bottl…
Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments
Junjie Ye, Changhao Jiang, Zhengyin Du +8
Effective tool use is essential for large language models (LLMs) to interact with their environment. However, progress is limited by the lack of efficient reinforcement learning (R…
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
ByteDance Seed, :, Jiaze Chen +267
We introduce Seed1.5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks. Seed1.5-Thinking achieves 8…
ToolHop: A Query-Driven Benchmark for Evaluating Large Language Models in Multi-Hop Tool Use
Junjie Ye, Zhengyin Du, Xuesong Yao +11
Effective evaluation of multi-hop tool use is critical for analyzing the understanding, reasoning, and function-calling capabilities of large language models (LLMs). However, progr…