Reward-guided search methods have demonstrated strong potential in enhancing tool-using agents by effectively guiding sampling and exploration over complex action spaces. As a core design, those search methods utilize process reward models (PRMs) to provide step-level rewards, enabling more fine-grained monitoring. However, there is a lack of systematic and reliable evaluation benchmarks for PRMs in tool-using settings. In this paper, we introduce ToolPRMBench, a large-scale benchmark specifi...
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---
name: toolprmbench-evaluating-and-advancing-process
title: "ToolPRMBench: Evaluating and Advancing Process Reward Models for Tool-Using Agents"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2601.12294"
keywords: [Agent, Tool]
description: "Reward-guided search methods have demonstrated strong potential in enhancing tool-using agents by effectively guiding sampling and exploration over complex action spaces. As a core design, those search methods utilize process reward models (PRMs) to provide step-level rewards, enabling more fine-grained monitoring. However, there is a lack of systematic and reliable evaluation benchmarks for PRMs in tool-using settings. In this paper, we introduce ToolPRMBench, a large-scale benchmark specifical..."
---
## Problem
ToolPRMBench addresses key challenges in autonomous agent development. This paper provides solutions for evaluating, building, or improving agent systems.
## Key Approach
The paper introduces a novel framework, methodology, or benchmark for toolprmbench. The core contributions include:
1. Systematic framework or benchmark for agent evaluation and development
2. Empirical findings on agent performance, efficiency, or capabilities
3. Generalizable principles applicable across domains
## When to Use
Use this skill when you need to:
- Evaluate or benchmark autonomous agent systems
- Understand best practices in agent design and evaluation
- Learn empirical results on agent performance
- Improve agent efficiency, reasoning, or capabilities
## When NOT to Use
- For non-agent-related tasks
- When seeking quick implementation code (see the paper for details)
- For general knowledge unrelated to autonomous agents
## Resources
- ArXiv Abstract: https://arxiv.org/abs/2601.12294
- Full PDF: https://arxiv.org/pdf/2601.12294
- HTML Version: https://arxiv.org/html/2601.12294
See the paper for comprehensive methodology, experimental protocols, benchmarks, and implementation details.
