7 citations · 12 across the 4 of their papers we have counts for
5 papers
AdaCtrl: Towards Adaptive and Controllable Reasoning via Difficulty-Aware Budgeting
Shijue Huang, Hongru Wang, Wanjun Zhong +4
Modern large reasoning models demonstrate impressive problem-solving capabilities by employing sophisticated reasoning strategies. However, they often struggle to balance efficienc…
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…
ReTool: Reinforcement Learning for Strategic Tool Use in LLMs
Jiazhan Feng, Shijue Huang, Xingwei Qu +6
While reasoning models (e.g., DeepSeek R1) trained with reinforcement learning (RL), excel in textual reasoning, they struggle in scenarios requiring structured problem-solving, su…
Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning
Chongyang Tao, Jiazhan Feng, Chang Liu +3
Establishing retrieval-based dialogue systems that can select appropriate responses from the pre-built index has gained increasing attention from researchers. For this task, the ad…
Learning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems
Jiazhan Feng, Chongyang Tao, Wei Wu +3
We study learning of a matching model for response selection in retrieval-based dialogue systems. The problem is equally important with designing the architecture of a model, but i…