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DeepResearch RewardBench: Evaluating Reward Models for Deep Research Report

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Briefings

This article introduces DeepResearch RewardBench, the first benchmark for evaluating reward models in deep research reports. The study collected diverse report candidates from multiple agents and scored them using query-specific golden rubrics. Extensive experiments show that while current reward models handle general coherence well, their performance significantly degrades when evaluating research reports. Generative LLMs acting as judges demonstrated comprehensive superiority over direct scoring methods.

Key points

• DeepResearch RewardBench is the first benchmark dedicated to evaluating reward modeling for research reports.
• Generative LLMs acting as judges demonstrated comprehensive superiority in evaluating research reports compared to direct scoring methods.

Context

Deep research systems integrate online information into reports through multi-step web searching and synthesis. Evaluating these reports presents a significant challenge due to their long context, high information density, and domain specificity, necessitating AI evaluation as a substitute for expensive human assessment.

Limitations

The benchmark comprises preference datasets formatted as pairs, triplets, and quadruplets. Extensive experiments demonstrate performance degradation when evaluating research reports specifically, though the exact failure mechanism and specific failure rates are not detailed in the evidence.

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