New technique helps LLMs rein in CoT lengths, optimizing reasoning without exploding compute costs

New technique helps LLMs rein in CoT lengths, optimizing reasoning without exploding compute costs

Carnegie Mellon University researchers propose a new LLM training technique that gives developers more control over chain-of-thought length.

New technique helps LLMs rein in CoT lengths, optimizing reasoning without exploding compute costs – English Times

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Reasoning through chain-of-thought (CoT) — the process by which models break problems into manageable “thoughts” before deducting answers — has become an integral part of the latest generation of frontier large language models (LLMs). However, the inference costs of reasoning models can quickly stack up as models generat…

New technique helps LLMs rein in CoT lengths, optimizing reasoning without exploding compute costs – RamaOnHealthcare

Reasoning through chain-of-thought (CoT) — the process by which models break problems into manageable “thoughts” before deducting answers — has become an