Generalization with systematically incorrect LLM priors

Determine whether the SCALAR framework generalizes to environments in which large language model priors about skill dependencies and task structure are systematically incorrect rather than merely imprecise.

Background

SCALAR is evaluated on Craftax and Craftax-Classic, domains where LLMs plausibly possess relevant prior knowledge from Minecraft-like data. Trajectory analysis in SCALAR can correct quantitative specification errors (e.g., resource counts), but the qualitative structure proposed by the LLM is broadly aligned with the environment.

The authors explicitly note that extending SCALAR to settings where LLM priors are systematically wrong (not just noisy) is unresolved, leaving open whether the framework’s refinement loop can correct qualitative specification errors and still yield effective skill learning.

References

However, generalization to domains where LLM priors are systematically incorrect rather than merely imprecise remains open.

— SCALAR: Learning and Composing Skills through LLM Guided Symbolic Planning and Deep RL Grounding  (2603.09036 - Zabounidis et al., 10 Mar 2026) in Appendix, Section "Limitations and Future Work"