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The Revolution of Token-Level Rewards
Accurate and customized AI models without the complexity.
This approach makes RL training significantly more effective for LLMs tackling complex tasks, by combining the memory efficiency and simplicity of GRPO with the token-sensitive rewards of PPO. As a result, we wanted to share this technique for others tuning LLMs for complex, high-stakes applications such as code generation, agentic workflows (where the model interacts with tools or environments), or structured reasoning tasks. By leveraging per-token rewards, you can speed up learning, ensure that good partial outputs are preserved, and make RL training practical even for smaller, more specialized models without requiring additional compute resources.
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