Author

В. Б. Терновський

2 papers indexed here

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#artificial intelligence Preprint Sep 2026

Closing the Loop: Practical Training Recipes for Looped Language Models

Looped language models increase effective depth by repeatedly applying a shared block of layers, but existing large-scale recipes require multi-stage training over trillions of tokens, while the benefits of recurrence remain difficult to separate from differences in data and training. In this work, we establish practic...

Andrei Marchenko, В. Е. Безруков, Oleg Kashurin et al. · 2 citations

#artificial intelligence Preprint Sep 2026

What Limits Recursive Reasoning Models: Optimization, Architecture and Test-Time Scaling

Recursive reasoning models apply a small shared Transformer block many times to refine a latent state. This gives them large effective depth with few parameters and makes them strong on algorithmic tasks. Such compact solvers are natural candidates for tools that an LLM can call on narrow algorithmic subproblems. Howev...

Yuliana Shakhvalieva, Dmitrii Kharchev, В. Е. Безруков et al. · 1 citation