rlxp-reward-tuning

What problem does it solve?

This Skill helps you rebalance an existing RL reward function when learning behavior is roughly correct but the reward terms are uneven, saturated, inactive, or overly dominant. It keeps tuning bounded to scalar adjustments instead of inventing new objectives.

Core Features & Use Cases

• Reward Diagnostics: Compare reward terms against task metrics and component logs to identify underweighted, overweighted, or stalled signals.
• Reflection-Guided Tuning: Use reward reflection artifacts to decide whether the fix belongs in reward weights, curriculum, or domain randomization instead of making blind changes.
• Safe Candidate Generation: Produce a bounded reward-tuning candidate with expected effects and risks for one task or study.
• Use Case: You have a policy that is learning, but one penalty is drowning out the rest of the reward; this Skill helps you isolate the imbalance and propose a limited fix.

Quick Start

Use the rlxp-reward-tuning skill to analyze the current reward definitions, logs, and reflection artifacts, then draft a bounded tuning candidate for the active RL task.