DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning
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2504.11456
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12
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6
Zhiwei He
zwhe99
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Natural Language Processing
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You provided three responses for each answer. Which one is correct, or are all of them correct?
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#2 opened 9 months ago
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arpitsh018
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#4 opened 8 months ago
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EnoMomo
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9 months ago
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#3 opened 9 months ago
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9 months ago
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't
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2503.16219
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52
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23
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't
Paper
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2503.16219
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Published
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52
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23
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't
Paper
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2503.16219
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Published
•
52
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23
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't
Paper
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2503.16219
•
Published
•
52
•
23
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't
Paper
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2503.16219
•
Published
•
52
•
23
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't
Paper
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2503.16219
•
Published
•
52
•
23
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't
Paper
•
2503.16219
•
Published
•
52
•
23
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't
Paper
•
2503.16219
•
Published
•
52
•
23
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't
Paper
•
2503.16219
•
Published
•
52
•
23
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't
Paper
•
2503.16219
•
Published
•
52
•
23
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't
Paper
•
2503.16219
•
Published
•
52
•
23
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn't
Paper
•
2503.16219
•
Published
•
52
•
23