ASPECT:Analogical Semantic Policy Execution via Language Conditioned Transfer

Researchers introduce ASPECT, a novel reinforcement learning framework that uses large language models as semantic operators to enable zero-shot transfer learning across novel tasks. By conditioning a text-based VAE on LLM-generated task descriptions, the approach allows agents to reuse policies on structurally similar but previously unseen tasks without discrete category constraints.

ASPECT addresses a fundamental challenge in reinforcement learning: the inability of trained agents to generalize to new tasks despite structural similarities. Traditional zero-shot transfer approaches rely on predefined discrete categories, creating rigid boundaries that fail when encountering novel or compositional task variations. This research demonstrates how semantic reasoning can bridge the generalization gap by dynamically remapping task descriptions through LLM inference.

The technical innovation centers on using an LLM as a runtime semantic operator that translates novel task observations into descriptions aligned with the agent's original training distribution. This remapped caption then conditions a text-based VAE to generate compatible state representations, enabling direct policy reuse. The approach represents a significant departure from fixed categorical systems by leveraging the flexible reasoning capabilities of large language models, which excel at analogical thinking and compositional understanding.

For the AI research community, this work has substantial implications for policy reuse and transfer learning efficiency. Rather than retraining agents for each new task variant, developers can deploy pre-trained policies that adapt through semantic transformation. This reduces computational costs and accelerates deployment timelines for reinforcement learning systems in dynamic environments.

Looking forward, the critical evaluation metric will be how broadly this approach generalizes across diverse task domains and how effectively LLM-generated descriptions capture task semantics. The reproducibility of results and code availability will determine whether this becomes a standard transfer learning technique in the RL community.

• →ASPECT replaces discrete task categories with continuous semantic space via LLM-conditioned VAE, enabling broader generalization
• →LLMs serve as dynamic semantic operators at test time to remap novel task observations to source-aligned descriptions
• →The approach achieves zero-shot transfer on structurally similar but compositionally novel tasks without predefined category mappings
• →Text-conditioned VAE generates state representations compatible with original training, enabling direct policy reuse without retraining
• →Research addresses core RL limitation of poor generalization to novel tasks despite structural similarity to training distribution