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This paper shows that autoregressive models can learn temporally-abstract actions within their internal representations to enable efficient hierarchical reinforcement learning.
By Seijin Kobayashi, Yanick Schimpf, Maximilian Schlegel and 12 more · arXiv 2512.20605 · PDF
UnverifiedSummarised by a model from the abstract; not yet checked by hand.
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About this paper
- Venue
- arXiv.org
- Citations
- 5, 0 influential · Semantic Scholar
- Upvotes
- 62 · Hugging Face
- Lab
- Google · on Companies · on Acquisitions · on Paydays · on TechConf · on Releases
Changes
This paper shows that autoregressive models can learn temporally-abstract actions within their internal representations to enable efficient hierarchical reinforcement learning.
Moved from Inference and efficiency to Reasoning.
A first count of 0 influential citations.
A first count of 5 citations.
Found by the weekly Hugging Face scan; not yet checked by hand.