Reinforcement Learning (RL) Guide - Group Relative Policy Optimization (GRPO), PDO, SFT, fine-tuning скачать в хорошем качестве

Reinforcement Learning (RL) Guide - Group Relative Policy Optimization (GRPO), PDO, SFT, fine-tuning

Reinforcement Learning (RL) Guide - Group Relative Policy Optimization (GRPO), PDO, SFT, fine-tuning These podcast / tutorial discusses advanced techniques for training and fine-tuning large language models (LLMs), with a particular focus on enhancing reasoning capabilities and computational efficiency. The "MedGemma Clinical Reasoning" and "Reinforcement Learning (RL) Guide" documents primarily focus on Group Relative Policy Optimization (GRPO), an RL method that optimizes models by iteratively improving outputs based on reward functions and verifiable feedback, often removing the need for traditional reward and value models. The "Open R1" and "Parallel LLM Training with Accelerate and Axolotl" texts explore various parallelism strategies like Data Parallelism (DP), Tensor Parallelism (TP), and Sequence Parallelism (SP) in conjunction with techniques like QLoRA and FSDP to enable training larger models on more accessible hardware, including consumer-grade GPUs. The articles emphasize how these methodologies, especially when combined, address the challenges of memory constraints, long context lengths, and the need for high-quality, verifiable rewards to cultivate sophisticated problem-solving behaviors in LLMs across diverse domains like medical diagnostics, mathematics, and code generation.