arXiv:2602.14814v3 Announce Type: replace Abstract: Over the last years, state-tracking tasks, particularly permutation composition, have become a testbed to understand the limits of sequence models architectures like Transformers and RNNs (linear and non-linear). However, these are often sequence-to-sequence tasks: learning to map actions (permutations) to states, which is incompatible with the next-token prediction setting commonly used to train language models. We address this gap by converting permutation composition into code via REPL traces that interleave state-reveals through prints an
The continuous drive to improve AI model efficiency and understanding of complex reasoning tasks, particularly state-tracking, leads to novel architectural approaches like that proposed here.
This research explores a new method for training sequence models to handle state-tracking, a critical capability for advanced AI agents and code generation, by converting it into a next-token prediction problem.
The ability to train state-tracking within the next-token prediction framework common to large language models could significantly enhance complex reasoning and potentially simplify programming for various AI applications.
- · AI researchers
- · Generative AI developers
- · Robotics
- · Software automation sector
- · Traditional symbolic AI approaches for state-tracking
- · Inefficient AI architectures
Improved performance of AI systems in tasks requiring sequential logical progress and state management.
Accelerated development of AI agents capable of more sophisticated planning and interaction with dynamic environments.
The simplification of programming complex AI behaviors by leveraging language model strengths, potentially democratizing advanced AI development.
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Read at arXiv cs.LG