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rlxp-reward-tuning

Adjust existing RL reward weights, thresholds, scales, or schedules within contract scope.

1|Updated May 14, 2026
Search Tags:#reinforcement learning#candidate generation#metric analysis#reward reflection#reward tuning#rl experiment
One-click install
npx skills add https://github.com/junhyekh/rlxp --skill rlxp-reward-tuning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rlxp-reward-tuning
Source: https://github.com/junhyekh/rlxp/tree/main/plugins/rl-experiment-assistant/skills/rlxp-reward-tuning
Command: npx skills add https://github.com/junhyekh/rlxp --skill rlxp-reward-tuning

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about rlxp-reward-tuning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fix unbalanced reinforcement learning reward signals without changing the action space?▼

To fix unbalanced reinforcement learning reward signals, adjust existing reward weights, thresholds, scales, or schedules without adding new reward terms. This rebalances uneven, saturated, or overly dominant penalties while preserving observations, terminations, action space, and evaluation semantics.

What is reward tuning in RL when behavior is roughly correct but reward terms are saturated?▼

Reward tuning in RL is the process of rebalancing an existing reward function by making scalar adjustments to weights, thresholds, or schedules. It addresses issues where behavior is roughly correct but reward terms are uneven, saturated, inactive, or overly dominant.

How do I diagnose which reward component is drowning out the rest of my RL task?▼

Diagnose dominant reward components by comparing reward terms against task metrics and component logs. This identifies underweighted, overweighted, or stalled signals, and uses reward reflection artifacts to decide whether the fix belongs in reward weights, curriculum, or domain randomization.

Can I add new reward terms to fix an inactive penalty during reward tuning?▼

You cannot add new reward terms during this reward tuning process. Tuning is bounded to scalar adjustments of existing reward weights, thresholds, scales, or schedules to ensure changes stay within the approved contract scope.

What do I need to start tuning reward weights for an RL experiment?▼

To start tuning reward weights, you need existing reward definitions, component logs, analysis data, and reward reflection artifacts. The task must already have reward components and roughly correct learning behavior that requires rebalancing.

When should I not use bounded reward weight adjustments for reinforcement learning?▼

You should not use bounded reward weight adjustments when you need to add new reward objectives, or when learning behavior is fundamentally incorrect. This approach only applies when reward components exist and behavior is roughly correct but unbalanced.

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