Breaking the UED Bottleneck: PACE Locates the Reinforcement‑Learning Zone of Proximal Development

The paper introduces PACE, a Parameter‑Change based Unsupervised Environment Design method that evaluates training levels by the magnitude of induced policy‑parameter updates, offering a low‑variance, computationally cheap signal that consistently outperforms prior UED approaches on MiniGrid and Craftax benchmarks.

CraftaxCurriculum LearningICML 2026

0 likes · 11 min read

Breaking the UED Bottleneck: PACE Locates the Reinforcement‑Learning Zone of Proximal Development

Machine Heart

May 21, 2026 · Artificial Intelligence

Breaking the Traditional UED Bottleneck: Using RL to Precisely Locate the Zone of Proximal Development

The paper introduces PACE, a Parameter Change Environment Design method that evaluates training levels by measuring induced policy parameter updates, offering a low‑variance learning‑progress signal that outperforms prior UED approaches on MiniGrid and Craftax benchmarks, achieving higher success rates and more stable generalization.

CraftaxCurriculum LearningICML 2026

0 likes · 10 min read

Breaking the Traditional UED Bottleneck: Using RL to Precisely Locate the Zone of Proximal Development