Learning More by Training Less: Uncertainty-Guided Progressive Contraction for Efficient Reasoning Post-Training
arXiv:2610.12933v1 Announce Type: new Abstract: Large language models have achieved remarkable advances in complex reasoning. However, scaling reasoning post-training with ever-larger corpora can substantially increase training cost without proportional performance gains. In…
- 01.arXiv:2610.12933v1 Announce Type: new Abstract: Large language models have achieved remarkable advances in complex reasoning
- 02.However, scaling reasoning post-training with ever-larger corpora can substantially increase training cost without proportional performance gains
This material is provided as an extended analytical summary.
Read full article at sourceRelated Intelligence
All stories →Anthropic is burning gas to feed the AI boom
As it prepares to go public, Anthropic's S-1 filing makes generative AI's methane dependency glaringly obvious. "Competition for access to electrical supply and data center locations with adequate power availability is intense." In documents filed with the US Securities and…
GPT-6.1 Terra halves OpenAI's API prices and still gains ground on its larger models
OpenAI has released GPT-6.1 Terra, a mid-range model that cuts API prices in half compared to its predecessor. Analysts say it sets a new standard for intelligence per dollar, especially for everyday computing tasks and AI-driven computer control. The article GPT-6.1 Terra…
How Sullivan & Cromwell builds legal tools with Codex | OpenAI
At Sullivan & Cromwell LLP, John Savva uses Codex to build tools around the firm’s legal expertise. Agreement Analyzer brings together a draft agreement, selected precedents, and playbooks reflecting the firm’s and client’s positions. It identifies potential issues and returns a…