Reinforcement Learning (RL) is a pivotal stage in the training of large language models (LLMs), enabling them to learn effectively from experiences rather than just from explicit labels. Unlike the earlier stages of pre-training and supervised fine-tuning, which are more structured, RL allows models to explore token sequences and receive feedback on their outputs. This process fosters better alignment with human intent and leverages the inherent stochastic nature of LLMs, facilitating the generation of diverse response paths. Ultimately, RL enhances the model's adaptability and effectiveness in producing accurate outputs across varied contexts.
RL allows LLMs to learn from experience rather than just labels, making it essential for aligning outputs with human intent.
The stochastic nature of LLMs means their responses vary even with the same prompt, enabling the generation of a wide array of possible outputs.
#reinforcement-learning #large-language-models #training-pipeline #human-ai-interaction #stochastic-processes
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