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ljvmiranda921.github.io | ||
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qwenlm.github.io
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| | | | | PAPER DISCORD Introduction Reinforcement Learning (RL) has emerged as a pivotal paradigm for scaling language models and enhancing their deep reasoning and problem-solving capabilities. To scale RL, the foremost prerequisite is maintaining stable and robust training dynamics. However, we observe that existing RL algorithms (such as GRPO) exhibit severe instability issues during long training and lead to irreversible model collapse, hindering further performance improvements with increased compute. To enable successful RL scaling, we propose the Group Sequence Policy Optimization (GSPO) algorithm. | |
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research.google
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| | | | | Posted by Ming-Wei Chang and Kelvin Guu, Research Scientists, Google Research Recent advances in natural language processing have largely built upo... | |
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amatria.in
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| | | | | Everything ends, many things start again | |
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futurism.com
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| | | Microsoft just invested $1 billion into OpenAI, which will now try to develop artificial general intelligence for Microsoft's cloud services. | ||
