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cs.LG2025
Omni-Thinker: Scaling Multi-Task RL in LLMs with Hybrid Reward and Task Scheduling
Derek Li, Jiaming Zhou, Leo Maxime Brunswic +8
The pursuit of general-purpose artificial intelligence depends on large language models (LLMs) that can handle both structured reasoning and open-ended generation. We present Omni-…
cs.LG2024★ 1 cited
MindStar: Enhancing Math Reasoning in Pre-trained LLMs at Inference Time
Jikun Kang, Xin Zhe Li, Xi Chen +9
Although Large Language Models (LLMs) achieve remarkable performance across various tasks, they often struggle with complex reasoning tasks, such as answering mathematical question…