4 papers
On Effectiveness and Efficiency of Agentic Tool-calling and RL Training
Tong Liu, Cheng Qian, Matej Cief +4
Tool-calling is a central component of modern large language model (LLM) agents, equipping them with skills beyond their parametric knowledge. This paper studies tool-calling along…
AlphaApollo: A System for Deep Agentic Reasoning
Zhanke Zhou, Chentao Cao, Xiao Feng +15
We present AlphaApollo, an agentic reasoning system that targets two bottlenecks in foundation-model reasoning: (1) limited reasoning capacity for complex, long-horizon problem sol…
Plan before Solving: Problem-Aware Strategy Routing for Mathematical Reasoning with LLMs
Shihao Qi, Jie Ma, Ziang Yin +5
Existing methods usually leverage a fixed strategy, such as natural language reasoning, code-augmented reasoning, tool-integrated reasoning, or ensemble-based reasoning, to guide L…
From Static to Dynamic: Adaptive Monte Carlo Search for Mathematical Process Supervision
Jie Ma, Shihao Qi, Rui Xing +4
The quality of process data plays a key role in training a Process Reward Model (PRM), which can enhance the complex mathematical reasoning capability of large language models. Exi…