Better exploration with parameter noise
9a
- Publicado
- Coletado
We’ve found that adding adaptive noise to the parameters of reinforcement learning algorithms frequently boosts performance. This exploration method is simple to implement and very rarely decreases performance, so it’s worth trying on any problem.
O overfeed.news indexa e aponta. Publicamos um trecho curto — o artigo completo fica em OpenAI News.
Mais de OpenAI News
Entre para seguir esta fonte
Franchisee reconciles cash in 94% less time with ChatGPT Work
An independent Schlotzsky’s and Cinnabon franchisee cut menu repricing time by 95% and built a workflow that could free up $100K in cash flow.
Terminal X matches senior investor judgment 88.6% with GPT-6.1
Terminal X’s Eval Agent grades outputs against a 54-criterion rubric, catching 25% more deviances from a fund’s mandate with GPT-6.1 Sol.
OpenAI acquires Orchestra Bio
OpenAI is acquiring Orchestra Bio to bring AI-powered operations to life sciences R&D, helping biopharma teams accelerate drug development and advance new treatments.
Advancing Coding in Low-Resource Programming Languages
Learn how OpenAI is improving ChatGPT across low-resource programming languages, with new evaluations for software engineering, SQLite, and COBOL migration.