1 citations · 2 across the 3 of their papers we have counts for
11 papers
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…
AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning
Zhiheng Xi, Jixuan Huang, Chenyang Liao +20
Developing autonomous LLM agents capable of making a series of intelligent decisions to solve complex, real-world tasks is a fast-evolving frontier. Like human cognitive developmen…
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…
VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks
Yu Yue, Yufeng Yuan, Qiying Yu +24
We present VAPO, Value-based Augmented Proximal Policy Optimization framework for reasoning models., a novel framework tailored for reasoning models within the value-based paradigm…
Recitation over Reasoning: How Cutting-Edge Language Models Can Fail on Elementary School-Level Reasoning Problems?
Kai Yan, Yufei Xu, Zhengyin Du +4
The rapid escalation from elementary school-level to frontier problems of the difficulty for LLM benchmarks in recent years have weaved a miracle for researchers that we are only i…