| 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:
- Systematic framework or benchmark for agent evaluation and development
- Empirical findings on agent performance, efficiency, or capabilities
- 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
See the paper for comprehensive methodology, experimental protocols, benchmarks, and implementation details.
