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🚨 New Paper Alert 🚨 We found that Supervised Fine-tuning on ONE problem can achieve similar performance gain as RL on ONE problem with 20x less compute! Paper: https://t.co/K5cxDNs1Gu Recently, people have shown that RL can work even with ONE example. This indicates that the… https://t.co/p9d2OwgIGo
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"text": "🚨 New Paper Alert 🚨\n\nWe found that Supervised Fine-tuning on ONE problem can achieve similar performance gain as RL on ONE problem with 20x less compute! \nPaper: https://t.co/jZE7cRG1tg\n\nRecently, people have shown that RL can work even with ONE example. This indicates that the strong reasoning capabilities were obtained during the pre-training stage, and RL can serve an effective approach to unleash these reasoning potential.\n⚠️ However, RL is expensive and unstable. Even RL on one example can consume more than 100 A100 GPUs hours. RL also suffers from various stability problems. \n\n🧠 Is there an easier approach to unleash the (general) reasoning potential from strong pre-trained LLMs?\nIt turns out that simple SFT (as it is) on minimum data doesn't work and causes severe overfitting issue. \n✅ But critique fine-tuning (CFT) on ONE problem can work! With 20x less compute (5 GPUs hours), it matches and even surpasses the performance of one-shot-RLVR (https://t.co/8a695kUyWM).\n\nIn the paper, we show that CFT on 1 problem can boost the average accuracy of six mathematical benchmarks (MATH-500, AMC, OlympiadBench, etc) by 5-15% across different-sized models. \nWe further test on logic reasoning tasks from BBEH like causal reasoning, disambiguation, etc and show similar performance gain of 15%. This shows the generalization of one-shot CFT beyond math.\n\n🎯 Therefore, we believe CFT works as a more efficient approach to unleash the hidden reasoning capabilities of the pre-trained LLMs!\n\nWebsite: https://t.co/2WDnrSYS0o\nHF collections: https://t.co/JwwSa37Jlw\nEverything is open-sourced.",
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