: A 2025 framework designed to improve Large Language Models' adherence to task constraints in planning problems like "BlocksWorld" and "TravelPlanner".
: Weights for the CaRL planner, which is noted as a top open-source RL planner for urban driving scenarios. File: C.A.R.L.v1.0.zip ...
: A 2026 model that uses RL to generate adversarial code samples to test the vulnerabilities of programming language models. (2025) paper? : A 2025 framework designed to improve Large
The zip file typically contains the codebase and pre-trained weights for the following: (2025) paper
: Bernhard Jaeger, Daniel Dauner, Jens Beißwenger, Simon Gerstenecker, Kashyap Chitta, and Andreas Geiger
: The paper introduces CaRL , a reinforcement learning (RL) planner for autonomous driving. It achieves state-of-the-art results on benchmarks like longest6 v2 and nuPlan , significantly outperforming prior methods such as "Roach" by reducing collisions and improving planning efficiency. Software and Resources
: Proceedings of the Conference on Robot Learning (CoRL), 2025