Latent Policy Steering through One-Step Flow Policies
Offline reinforcement learning (RL) allows robots to learn from offline datasets without risky exploration. Yet, offline RL's performance often hinges on a brittle trade-off between (1) return maximization, which can push policies outside the dataset support, and (2) behavioral constraints, which typically require sensitive hyperparameter tuning. Latent steering offers a structural way to stay within the dataset support during RL, but existing offline adaptations commonly approximate action values using latent-space critics learned via indirect distillation, which can lose information and hinder convergence. We propose Latent Policy Steering (LPS), which enables high-fidelity latent polic...
| Authors | Hokyun Im, Andrey Kolobov, Jianlong Fu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 5 March 2026 |
| Last updated at source | 8 July 2026 |
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