Meta's EvoHarness-RL Trains Agents to Decide When to Use Memory

Meta's new paper argues that simply adding memory and tools to agents is insufficient, and proposes EvoHarness-RL, a reinforcement learning method that trains agents—built on Qwen2.5—to decide when to invoke external state in long-horizon tasks.

2026-08-24 ~ 2026-08-24 · 2 related posts

• Meta Paper: Training Agents to Decide When to Use Memory via RL — rohanpaul_ai · 2026-08-24
• Meta paper: Agents must learn when to use tools, not just have them, achieving 96.9% on ALFWorld — daniel_mac8 · 2026-08-24