Sign up to save your podcasts
Or
Share [QA] L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
[QA] L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning
The paper introduces Length Controlled Policy Optimization (LCPO) for training reasoning models, enabling controlled output length and improved performance, outperforming existing methods while allowing for efficient compute allocation.
https://arxiv.org/abs//2503.04697
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
View all episodes
By Igor Melnyk
5
33 ratings
The paper introduces Length Controlled Policy Optimization (LCPO) for training reasoning models, enabling controlled output length and improved performance, outperforming existing methods while allowing for efficient compute allocation.
https://arxiv.org/abs//2503.04697
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
More shows like Arxiv Papers
View all
FT News Briefing
699 Listeners
Google DeepMind: The Podcast
199 Listeners
Last Week in AI
288 Listeners
Latent Space: The AI Engineer Podcast
76 Listeners
The AI Daily Brief (Formerly The AI Breakdown): Artificial Intelligence News and Analysis
441 Listeners