RL

24 May

Training Diffusion Models with Reinforcement Learning

stp2y0 CommentsAIdiffusion, finetuning, learning, reinforcement, RL, stable diffusion

Training Diffusion Models with Reinforcement Learning replay Diffusion models have recently emerged as the de facto standard for generating complex, high-dimensional outputs. You may know them for their ability to produce stunning AI art and hyper-realistic synthetic images, but they have also found success in other applications such as drug design and continuous control. The key idea behind diffusion models is to iteratively transform random noise into a sample, such as an image or protein structure. This is typically motivated as a maximum likelihood estimation problem, where the model is trained to generate samples that match the training data as closely as…

24 May

Rethinking the Role of PPO in RLHF

stp2y0 CommentsAIalignment, finetuning, learning, reinforcement, RL, RLHF

Rethinking the Role of PPO in RLHF TL;DR: In RLHF, there’s tension between the reward learning phase, which uses human preference in the form of comparisons, and the RL fine-tuning phase, which optimizes a single, non-comparative reward. What if we performed RL in a comparative way? Figure 1: This diagram illustrates the difference between reinforcement learning from absolute feedback and relative feedback. By incorporating a new component - pairwise policy gradient, we can unify the reward modeling stage and RL stage, enabling direct updates based on pairwise responses. Large Language Models (LLMs) have powered increasingly capable virtual assistants, such as…