A small parametric transition model plus a Jaccard consistency gate grounds LLM agent state deltas, cutting hallucinated-state rate ~80% and raising success from 0.668 to 0.838 on graph planning benchmarks.
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LaGO improves online RL success rates over vanilla PPO by using pretrained LLMs as latent action priors, raising rates from 15.1% to 27.2% on CLEVR-Robot and 2.7% to 15.2% on Meta-World.
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Agent vs. Parametric World Models: Hybrid Planning for Reliable Language Agents
A small parametric transition model plus a Jaccard consistency gate grounds LLM agent state deltas, cutting hallucinated-state rate ~80% and raising success from 0.668 to 0.838 on graph planning benchmarks.
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LaGO: Latent Action Guidance for Online Reinforcement Learning
LaGO improves online RL success rates over vanilla PPO by using pretrained LLMs as latent action priors, raising rates from 15.1% to 27.2% on CLEVR-Robot and 2.7% to 15.2% on Meta-World.