AI RESEARCH
Reflective Prompted Policy Optimization: Trajectory-Grounded Revision and Salience Bias
arXiv CS.LG
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ArXi:2605.08315v1 Announce Type: new Existing LLM-based policy optimizers see only scalar rewards: that a policy scored 0.45, but not whether the agent got stuck in a loop, fell into a hole on the third step, or performed well on 19 out of 20 rollouts and failed catastrophically on one. We propose Reflective Prompted Policy Optimization (R2PO), a two-stage LLM framework for policy search over compact policy classes that augments scalar reward feedback with trajectory-level behavioral evidence.