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Reinforcement Learning Won Again, This Time With Microsoft
- Microsoft's Phi family of models, part of their AI research, includes lightweight and high-performing models that outshine competitors.
- Phi-4 Reasoning, a 14 billion-parameter model, was improved using supervised fine-tuning and reinforcement learning.
- These models excel in coding, math, and scientific tasks, surpassing larger models like DeepSeek R1.
- The success is attributed to high-quality training datasets with over 1.4 million prompts and answers generated by OpenAI.
- Reinforcement learning (RL) in Phi models allows for varied answers as long as the outcome is correct.
- The RL process in Microsoft's models focuses on mathematical reasoning by incentivising correctness and proper formatting.
- RL with Microsoft models requires less data compared to supervised fine-tuning, improving accuracy across evaluations.
- Despite successes, challenges remain, including resource consumption, slower response times, and contradictions in reasoning steps.
- Issues like reward hacking and discrepancies between reasoning chains and actual processes raise concerns in the AI community.
- Efforts to enhance interpretability and safety of reasoning models continue, aiming to understand model behaviors and improve overall performance.
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