rlxp-reward-engineering

What problem does it solve?

This Skill helps you design or patch reward logic for one reinforcement-learning task when the current reward terms cannot express the desired behavior and the study contract allows reward-code changes.

Core Features & Use Cases

• Minimal reward edits: Propose the smallest safe change that adds the missing signal without rewriting the whole objective.
• Risk-aware review: Check for reward hacking, privileged signals, scale issues, and monitoring gaps before any patch is drafted.
• Evidence-gated workflow: Use it when reward saturation, sparse behavioral signals, or training-reward divergence suggests scalar tuning is not enough.
• Use case: A model keeps gaming a proxy reward, so you inspect the reward components, confirm the issue in analyses, and draft a bounded patch plan with sanity checks.

Quick Start

Ask the assistant to review the active study’s reward code, analyses, and contract permissions, then draft the smallest safe reward change for the current RL task.