Track manipulation, locomotion and embodied systems.
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Affiliation:, University of Montreal/ Mila
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Restricting LLM fine-tuning to the bottom singular subspace nearly eliminates catastrophic forgetting, even when protecting 99% of weights, while maintaining strong target-task performance.
Looped LLMs don't just perform better reasoning, they also internally mirror the distinct inference stages of standard feedforward models, repeating them cyclically.
By recursively aggregating reasoning chains, even smaller LLMs can now achieve performance competitive with much larger models, challenging the assumption that scale is the only path to improved reasoning.