Reflective Prompted Policy Optimization uses a Critic-LLM to inspect full trajectories and propose grounded revisions, yielding higher mean best rewards, faster near-optimal performance, and greater stability than scalar-reward baselines across ten environments.
Switched to action 2, which keeps the agent within the goal corridor (up to 7 or down to 15) and never reaches the hole directly
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Reflective Prompted Policy Optimization: Trajectory-Grounded Revision and Salience Bias
Reflective Prompted Policy Optimization uses a Critic-LLM to inspect full trajectories and propose grounded revisions, yielding higher mean best rewards, faster near-optimal performance, and greater stability than scalar-reward baselines across ten environments.