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• ProAct significantly improves planning accuracy in both stochastic and deterministic environments.result 90%
• A 4B parameter model trained with ProAct outperforms all open-source baselines and rivals state-of-the-art closed-source models.result 90%
• ProAct demonstrates robust generalization to unseen environments.result 85%
• Grounded LookAhead Distillation (GLAD) enables agents to learn foresight logic without computational overhead of inference-time search.method 85%
• Monte-Carlo Critic (MC-Critic) provides low-variance value estimates that facilitate stable policy optimization.technical 80%
• ProAct addresses compounding errors in long-horizon planning for LLM agents in interactive environments.technical 90%
• Scaling to more complex environments might introduce unforeseen challenges for ProAct.limitation 70%
• ProAct's effectiveness could be limited by dependency on quality of initial environment data for training.limitation 70%

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