Computer Science > Machine Learning
arXiv:2603.09036 (cs)
[Submitted on 10 Mar 2026]
Title:SCALAR: Learning and Composing Skills through LLM Guided Symbolic Planning and Deep RL Grounding
Authors:Renos Zabounidis, Yue Wu, Simon Stepputtis, Woojun Kim, Yuanzhi Li, Tom Mitchell, Katia Sycara
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Abstract:LM-based agents excel when given high-level action APIs but struggle to ground language into low-level control. Prior work has LLMs generate skills or reward functions for RL, but these one-shot approaches lack feedback to correct specification errors. We introduce SCALAR, a bidirectional framework coupling LLM planning with RL through a learned skill library. The LLM proposes skills with preconditions and effects; RL trains policies for each skill and feeds back execution results to iteratively refine specifications, improving robustness to initial errors. Pivotal Trajectory Analysis corrects LLM priors by analyzing RL trajectories; Frontier Checkpointing optionally saves environment states at skill boundaries to improve sample efficiency. On Craftax, SCALAR achieves 88.2% diamond collection, a 1.9x improvement over the best baseline, and reaches the Gnomish Mines 9.1% of the time where prior methods fail entirely.
| Comments: | |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2603.09036 [cs.LG] |
| (or arXiv:2603.09036v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09036
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Submission history
From: Renos Zabounidis [view email][v1] Tue, 10 Mar 2026 00:11:58 UTC (21,022 KB)
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