7 papers
Bridging the Detection-to-Abstention Gap in Reasoning Models under Insufficient Information
Renjie Gu, Jiaxu Li, Yihao Wang +8
We highlight a failure mode of large reasoning models on questions with insufficient information: models may recognize that a problem is under-specified, yet still continue reasoni…
Hi-WM: Human-in-the-World-Model for Scalable Robot Post-Training
Yaxuan Li, Zhongyi Zhou, Yefei Chen +5
Post-training is essential for turning pretrained generalist robot policies into reliable task-specific controllers, but existing human-in-the-loop pipelines remain tied to physica…
dWorldEval: Scalable Robotic Policy Evaluation via Discrete Diffusion World Model
Yaxuan Li, Zhongyi Zhou, Yefei Chen +2
Evaluating robotics policies across thousands of environments and thousands of tasks is infeasible with existing approaches. This motivates the need for a new methodology for scala…
SpecForge: A Flexible and Efficient Open-Source Training Framework for Speculative Decoding
Shenggui Li, Chao Wang, Yikai Zhu +14
Large language models incur high inference latency due to sequential autoregressive decoding. Speculative decoding alleviates this bottleneck by using a lightweight draft model to…
Enhancing Agentic RL with Progressive Reward Shaping and Value-based Sampling Policy Optimization
Jianghao Su, Xia Zeng, Luhui Liu +3
Large Language Models (LLMs) empowered with Tool-Integrated Reasoning (TIR) can iteratively plan, call external tools, and integrate returned information to solve complex, long-hor…
IKFST: IOO and KOO Algorithms for Accelerated and Precise WFST-based End-to-End Automatic Speech Recognition
Zhuoran Zhuang, Ye Chen, Chao Luo +5
End-to-end automatic speech recognition has become the dominant paradigm in both academia and industry. To enhance recognition performance, the Weighted Finite-State Transducer (WF…
CoDA: A Context-Decoupled Hierarchical Agent with Reinforcement Learning
Xuanzhang Liu, Jianglun Feng, Zhuoran Zhuang +7
Large Language Model (LLM) agents trained with reinforcement learning (RL) show great promise for solving complex, multi-step tasks. However, their performance is often crippled by…