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[QA] L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning episode artwork

Mar 8, 2025 8 min

[QA] L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

from Arxiv Papers Hosted by Igor Melnyk

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

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[QA] L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

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