Multiscale optimization for long-horizon LLM agents
In brief
- Long-horizon LLM agents generate actions token by token over full turns.
- Multiscale Policy Optimization (MPO) improves credit allocation between tokens and turns.
- Consistent gains are observed on search, tool-use, and automation tasks with 4B and 30B models.
Long-horizon LLM agents interact with environments through full assistant turns while generating each action token by token. The study finds critic values vary mainly across turns, supporting optimization anchored at turns with finer credit allocation among tokens. The authors introduce Multiscale Policy Optimization (MPO), combining Multiscale Advantage Estimation (MAE) and turn-level importance sampling to align credit assignment and policy updates at the turn scale. Experiments on search, tool-use, and automation tasks with 4B and 30B models show consistent gains over strong RL baselines according to the announcement.
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