8 papers · 1 filter
DPEPO: Diverse Parallel Exploration Policy Optimization for LLM-based Agents
Junshuo Zhang, Chengrui Huang, Feng Guo +6
Large language model (LLM) agents that follow the sequential "reason-then-act" paradigm have achieved superior performance in many complex tasks.However, these methods suffer from…
HTAA: Enhancing LLM Planning via Hybrid Toolset Agentization & Adaptation
Chengrui Huang, Junshuo Zhang, Zhiyuan Ma +7
Enabling large language models to scale and reliably use hundreds of tools is critical for real-world applications, yet challenging due to the inefficiency and error accumulation i…
PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning
Ruixiang Feng, Yuntao Wen, Silin Zhou +14
Language Reasoning Models (LRMs) achieve strong performance by scaling test-time computation but often suffer from ``overthinking'', producing excessively long reasoning traces tha…
Evolution without Large Models: Training Language Model with Task Principles
Minghang Zhu, Shen Gao, Zhengliang Shi +5
A common training approach for language models involves using a large-scale language model to expand a human-provided dataset, which is subsequently used for model training.This me…
CulFiT: A Fine-grained Cultural-aware LLM Training Paradigm via Multilingual Critique Data Synthesis
Ruixiang Feng, Shen Gao, Xiuying Chen +2
Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, yet they often exhibit a specific cultural biases, neglecting the values and linguistic…
TTPA: Token-level Tool-use Preference Alignment Training Framework with Fine-grained Evaluation
Chengrui Huang, Shen Gao, Zhengliang Shi +2
Existing tool-learning methods usually rely on supervised fine-tuning, they often overlook fine-grained optimization of internal tool call details, leading to limitations in prefer…