most citedEnhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2025

UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning

Haoming Wang, Haoyang Zou, Huatong Song +109

The development of autonomous agents for graphical user interfaces (GUIs) presents major challenges in artificial intelligence. While recent advances in native agent models have sh…

cs.CL2025★ 1 cited

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…

cs.LG2024

Flaming-hot Initiation with Regular Execution Sampling for Large Language Models

Weizhe Chen, Zhicheng Zhang, Guanlin Liu +6

Since the release of ChatGPT, large language models (LLMs) have demonstrated remarkable capabilities across various domains. A key challenge in developing these general capabilitie…

cs.AI2024

Process Supervision-Guided Policy Optimization for Code Generation

Ning Dai, Zheng Wu, Renjie Zheng +7

Reinforcement learning (RL) with unit test feedback has enhanced large language models' (LLMs) code generation, but relies on sparse rewards provided only after complete code evalu…

cs.LG2024★ 2 cited

Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization

Kaixuan Ji, Guanlin Liu, Ning Dai +6

Reinforcement Learning (RL) plays a crucial role in aligning large language models (LLMs) with human preferences and improving their ability to perform complex tasks. However, curr…