Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling
In classical Reinforcement Learning from Human Feedback (RLHF), Reward Models (RMs) serve as the fundamental signal provider for model alignment. As Large Language Models evolve into agentic systems capable of autonomous tool invocation and complex reasoning, the paradigm of reward modeling faces unprecedented challenges -- most notably, the lack of benchmarks specifically designed to assess RM capabilities within tool-integrated environments. To address this gap, we present Plan-RewardBench, a
Record details
Published: 9 April 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Agents & autonomy · Environment
Retrieved: 14 July 2026
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ethics.ai (9 April 2026), “Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling,” evidence record 6129, https://ethics.ai/record/6129 (originally published by arXiv).
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