10 papers
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…
Generated Contents Enrichment
Mahdi Naseri, Jiayan Qiu, Zhou Wang
We study Generated Contents Enrichment (GCE), a conditional image-generation task in which a sparse scene description is first enriched through an explicit scene representation and…
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…
Remodeling Semantic Relationships in Vision-Language Fine-Tuning
Xiangyang Wu, Liu Liu, Baosheng Yu +2
Vision-language fine-tuning has emerged as an efficient paradigm for constructing multimodal foundation models. While textual context often highlights semantic relationships within…
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…
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…