11 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…
StepGuard: Guarding Web Navigation via Single-Step Calibration
Zhihao Cui, Yuchen Zhang, Xiyang Sun +6
Web navigation requires agents to follow natural language goals, interact with web pages, and produce accurate answers. While recent advances leverage vision-language models and re…
OpenClaw-Skill: Collective Skill Tree Search for Agentic Large Language Models
Tianyi Lin, Chuanyu Sun, Jingyi Zhang +6
Equipping Large Language Model (LLM) agents with effective skills is crucial for solving complex tasks in real-world systems like OpenClaw. In this work, we aim to develop a framew…
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…
Towards Unified Multimodal Misinformation Detection in Social Media: A Benchmark Dataset and Baseline
Haiyang Li, Yaxiong Wang, Shengeng Tang +6
Detecting deceptive multimodal content on social media has become an increasingly important problem. Two major types of deception dominate: human-crafted misinformation (e.g., rumo…
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…
LARGO: Low-Rank Regulated Gradient Projection for Robust Parameter Efficient Fine-Tuning
Haotian Zhang, Liu Liu, Baosheng Yu +3
The advent of parameter-efficient fine-tuning methods has significantly reduced the computational burden of adapting large-scale pretrained models to diverse downstream tasks. Howe…
SIGMA: Refining Large Language Model Reasoning via Sibling-Guided Monte Carlo Augmentation
Yanwei Ren, Haotian Zhang, Fuxiang Wu +4
Enhancing large language models by simply scaling up datasets has begun to yield diminishing returns, shifting the spotlight to data quality. Monte Carlo Tree Search (MCTS) has eme…