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Spurious Rewards: Rethinking Training Signals in RLVR
Reinforcement learning with verifiable rewards (RLVR) enhances mathematical reasoning in Qwen2.5-Math, achieving notable performance improvements, but spurious rewards may not benefit other models like Llama3 or OLMo2.
https://arxiv.org/abs//2506.10947
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
Reinforcement learning with verifiable rewards (RLVR) enhances mathematical reasoning in Qwen2.5-Math, achieving notable performance improvements, but spurious rewards may not benefit other models like Llama3 or OLMo2.
https://arxiv.org/abs//2506.10947
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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