এই এপিসোড সম্বন্ধে
In this episode, Brandon Cui, Research Scientist at MosaicML and Databricks, dives into cutting-edge advancements in AI model optimization, focusing on Reward Models and Reinforcement Learning from Human Feedback (RLHF).
Highlights include:
- How synthetic data and RLHF enable fine-tuning models to generate preferred outcomes.
- Techniques like Policy Proximal Optimization (PPO) and Direct Preference
Optimization (DPO) for enhancing response quality.
- The role of reward models in improving coding, math, reasoning, and other NLP tasks.
Connect with Brandon Cui:
https://www.linkedin.com/in/bcui19/
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অতীতের এপিসোডগুলি অনুসন্ধান করুন
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দাবি পরিত্যাগ: এই পৃষ্ঠায় এম্বেড করা পডকাস্ট এবং আর্টওয়ার্ক Databricks থেকে এসেছে, যা এর মালিকের সম্পত্তি এবং Listen Notes, Inc দ্বারা অনুমোদিত বা অনুমোদিত নয়৷
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