blog post 123
By | June 1, 2023
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The architecture of a Reinforcement Learning (RL) library is split into two primary components: Generation and Training. During the generation phase, an LLM Engine performs multi-turn rollouts within an environment to produce data and reward signals. This output is then fed into the training phase to update the model's parameters. This process forms a feedback loop, where the progressively improved model generates the next iteration of data for continuous refinement.Open Source RL Libraries for LLMs
Figure 24: TFCC inference runtimeLarge-Scale Deployment of Ray in Tencent’s Weixin AI Infrastructure
