rlxp-curriculum-design

What problem does it solve?

This Skill helps you tune learning distribution instead of reward so reinforcement-learning systems can improve on hard cases without changing the final evaluation goal.

Core Features & Use Cases

• Curriculum Diagnosis: Compare easy and hard performance using per-bin evidence to identify where learning stalls.
• Bounded Curriculum Changes: Propose sampling reweighting, difficulty schedules, or environment-generation bridges that stay within contract scope.
• Evaluation Safety: Preserve final success metrics and held-out evaluation semantics while adapting training exposure.
• Use Case: When a policy succeeds on simple scenes but fails on difficult ones, use this Skill to decide whether to rebalance samples, ease progression, or add intermediate scene families.

Quick Start

Ask the assistant to review the curriculum bins, compare easy-versus-hard performance, and propose a bounded sampling or difficulty change that preserves the final evaluation metric.