SCALAR: Learning and Composing Skills through LLM Guided Symbolic Planning and Deep RL Grounding
arXiv:2603.09036v1 Announce Type: new 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.
arXiv:2603.09036v1 Announce Type: new 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.
Executive Summary
The article introduces SCALAR, a novel framework that integrates Large Language Models (LLMs) with Deep Reinforcement Learning (RL) to learn and compose skills. SCALAR enables LLMs to propose skills with preconditions and effects, while RL trains policies for each skill and provides feedback to refine specifications. This bidirectional approach improves robustness to initial errors and achieves state-of-the-art results in various environments, including Craftax and Gnomish Mines.
Key Points
- ▸ SCALAR combines LLM planning with RL through a learned skill library
- ▸ The framework enables feedback from RL to correct LLM specification errors
- ▸ SCALAR achieves significant improvements over baseline methods in various environments
Merits
Improved Robustness
SCALAR's bidirectional approach allows for iterative refinement of specifications, improving robustness to initial errors
Enhanced Sample Efficiency
Frontier Checkpointing enables the saving of environment states at skill boundaries, reducing sample complexity
Demerits
Computational Complexity
The integration of LLMs and RL may increase computational requirements, potentially limiting scalability
Expert Commentary
SCALAR represents a significant advancement in the field of artificial intelligence, demonstrating the potential for integrating LLMs and RL to learn and compose complex skills. The framework's ability to provide feedback and refine specifications iteratively addresses a long-standing challenge in the field. However, further research is needed to address the computational complexity and explainability of the approach, as well as its potential applications and implications for various domains.
Recommendations
- ✓ Future research should investigate the application of SCALAR to real-world domains, such as robotics and healthcare, to demonstrate its practical potential
- ✓ The development of more efficient and scalable algorithms for integrating LLMs and RL is essential to facilitate the widespread adoption of SCALAR and similar frameworks