University of California, Merced

Adding a sparsity penalty to TD-JEPA's task encoder concentrates the inferred reward near the goal, raising average return on DMC by 53 points and average success rate on OGBench by 10.4 percentage points in zero-shot reinforcement learning.

Adding a sparsity penalty to TD-JEPA's task encoder concentrates the inferred reward near the goal, raising average return on DMC by 53 points and average success rate on OGBench by 10.4 percentage points in zero-shot reinforcement learning.

SkillForge turns an agent's skill library from an append-only store into a fitness-driven lifecycle of trial, active, stable, and retired states, so skills and policy co-evolve during reinforcement learning and reach the highest aggregate success rate across ALFWorld, WebShop, and Search-Augmented QA, up to 7.8% relative over the strongest baseline.

SkillForge turns an agent's skill library from an append-only store into a fitness-driven lifecycle of trial, active, stable, and retired states, so skills and policy co-evolve during reinforcement learning and reach the highest aggregate success rate across ALFWorld, WebShop, and Search-Augmented QA, up to 7.8% relative over the strongest baseline.