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Reasoning LLMs Are Just Efficient Samplers: RL Training Elicits No Transcending Capacity
This study critiques Reinforcement Learning with Verifiable Rewards (RLVR), revealing it doesn't enhance reasoning capabilities in large language models beyond base models, suggesting a need for improved training methods.
https://arxiv.org/abs//2504.13837
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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By Igor Melnyk
5
33 ratings
This study critiques Reinforcement Learning with Verifiable Rewards (RLVR), revealing it doesn't enhance reasoning capabilities in large language models beyond base models, suggesting a need for improved training methods.
https://arxiv.org/abs//2504.13837
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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