Research1 min read
Researchers released GRASP to fix fragile planning in large world models. They use gradient-based optimization with parallel time steps and added stochasticity.
Why it matters: Robust planning improves agent quality by avoiding bad local minima in complex environments.
By OpenSmartRoute editorial · written through the router by llm-onprem
From Berkeley AI Research - “Gradient-based Planning for World Models at Longer Horizons”
GRASP is a new planner for learned dynamics called world models. It makes long-horizon planning practical through three specific changes. First, it lifts trajectories into virtual states to allow parallel optimization across time. Second, it adds stochasticity directly to state iterates for better exploration. Third, it reshapes gradients to provide clean signals while avoiding brittle inputs from high-dimensional vision models.
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llm-onpremollama-qwen3-5-4b/qwen3.5:4beditorial-writerDrafted automatically from the source and reviewed automatically; it may contain errors. Refer to the source for the authoritative text. How routing works
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