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How LLM training with RL Environments works? It all starts with ๐ฅ๐ฒ๐ถ๐ป๐ณ๐ผ๐ฟ๐ฐ๐ฒ๐บ๐ฒ๐ป๐ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐๐ถ๐๐ต ๐ฉ๐ฒ๐ฟ๐ถ๐ณ๐ถ๐ฎ๐ฏ๐น๐ฒ ๐ฅ๐ฒ๐๐ฎ๐ฟ๐ฑ๐ - question asked - model generates reasoning + answer - answer checked against ground truth - reward drives RL training In this setup, the environment is simple: fixed questions and answers, rollout logic, reward(s) Consider a more complex tic-tac-toe env โโญ It adds: - dynamic game generation/handling - tunable opponent skill - multi-turn interactions (envs can also include tools) --- What happens at training? We use ๐๐ฟ๐ผ๐๐ฝ ๐ฅ๐ฒ๐น๐ฎ๐๐ถ๐๐ฒ ๐ฃ๐ผ๐น๐ถ๐ฐ๐ ๐ข๐ฝ๐๐ถ๐บ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป with a tic-tac-toe env No critic model needed, the group is the baseline Simpler than PPO 1๏ธโฃ Rollout generation: from the same board, model plays N games via sampling 2๏ธโฃ Each game scored with deterministic rewards (win, format, ...) 3๏ธโฃ Mean score computed across the group 4๏ธโฃ Each rollout's advantage = its score minus the group mean 5๏ธโฃ Model updated to favor trajectories above baseline ๐ Repeat For a deep dive, check out ๐ฑ https://github.com/anakin87/llm-rl-environments-lil-course a free hands-on course on RL environments for LLMs
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Shallow-ฯ: Knowledge Distillation for Flow-based VLAs
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eoe/Qwen2-0.5B-Instruct-Q4_0-GGUF
Text Generation
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0.5B
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Nov 4, 2024
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eoe/Qwen2-0.5B-Instruct-Q2_K-GGUF
Text Generation
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0.5B
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Nov 4, 2024
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eoe/mobilenetv2
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Jul 23, 2023