9 papers
One-shot Human Video-Guided Adaptation for Vision-Language-Action Models
Qi Chen, Yuwei Wu, Quan Chen +5
Vision-language-action (VLA) models have recently achieved remarkable progress by leveraging large-scale robot datasets and foundation models for general-purpose robot manipulation…
Branch2Skill: Efficient Skill Evolution Through Reasoning Trees
Yanwei Ren, Haotian Zhang, Likang Xiao +5
Skill evolution improves agent skills through feedback over time, with failed trajectories often providing informative signals by revealing incomplete or misleading behaviors. Howe…
SearchSwarm: Towards Delegation Intelligence in Agentic LLMs for Long-Horizon Deep Research
Xiaochong Lan, Quan Chen, Kun Tao +7
Large language models are increasingly expected to handle complex, long-horizon real-world tasks whose context demands can grow without bound, yet model context windows remain inhe…
Recycling Failures: Salvaging Exploration in RLVR via Fine-Grained Off-Policy Guidance
Yanwei Ren, Haotian Zhang, Likang Xiao +6
Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a powerful paradigm for enhancing the complex reasoning capabilities of Large Reasoning Models. However, standa…
SPOGW: a Score-based Preference Optimization method via Group-Wise comparison for workflows
Yitong Cui, Liu Liu, Baosheng Yu +5
Large language models (LLMs) have exhibited significant capabilities in addressing challenging problems throughout various fields, often through the use of agentic workflows that a…
IMAGINE: Integrating Multi-Agent System into One Model for Complex Reasoning and Planning
Xikai Zhang, Bo Wang, Likang Xiao +4
Although large language models (LLMs) have made significant strides across various tasks, they still face significant challenges in complex reasoning and planning. For example, eve…
ContextPRM: Leveraging Contextual Coherence for multi-domain Test-Time Scaling
Haotian Zhang, Liu Liu, Baosheng Yu +5
Process reward models (PRMs) have demonstrated significant efficacy in enhancing the mathematical reasoning capabilities of large language models (LLMs) by leveraging test-time sca…
Long-Horizon Visual Imitation Learning via Plan and Code Reflection
Quan Chen, Chenrui Shi, Qi Chen +6
Learning from long-horizon demonstrations with complex action sequences presents significant challenges for visual imitation learning, particularly in understanding temporal relati…
Instruction Learning Paradigms: A Dual Perspective on White-box and Black-box LLMs
Yanwei Ren, Liu Liu, Baosheng Yu +2
Optimizing instructions for large language models (LLMs) is critical for harnessing their full potential in complex and diverse tasks. However, relying solely on white-box approach…