May 29, 2023 · In this paper we introduce a new parameterization of the reward model in RLHF that enables extraction of the corresponding optimal policy in closed form, allowing us to solve the standard RLHF problem with only a simple classification loss.

To run DPO, use the same command as SFT, but pass loss=dpo, loss.beta=DESIRED_BETA (0.1-0.5 is a good starting point), and model.archive=/path/to/checkpoint/from/sft/step-XXXX/policy.pt. If SFT completed successfully, you should also have a /.../LATEST/policy.pt from the end of training.

Jan 18, 2024 · To avoid this, researchers at Google DeepMind introduced Identity Preference Optimisation (IPO), which adds a regularisation term to the DPO loss and enables one to train models to convergence without requiring tricks like early stopping.

Sep 4, 2024 · The core innovation in DPO lies in its reparameterization of the loss function used in RLHF. By changing the variables in the loss equation, DPO directly influences the model’s policy—essentially, a model's strategy to decide its outputs based on input data.

Nov 5, 2023 · Direct Preference Optimization is a stable, performant, and computationally lightweight algorithm. Unlike its predecessor, RLHF, DPO eliminates the need for fitting a reward model, sampling...

Dec 22, 2024 · Loss function. Mathematically, DPO fine-tunes the LLM by maximizing the margin between the probability of the preferred and rejected response. The loss function is:

The key insight in Direct Preference Optimization is replacing the complex reward modeling process in RLHF with a simple loss function that directly optimizes for human preferences in closed form.

Jan 2, 2024 · Direct Preference Optimization (DPO) is fundamentally a streamlined approach for fine-tuning substantial language models such as Mixtral 8x7b, Llama2, and even GPT4. It’s useful because it...

Direct Preference Optimization (DPO) loss [1]. The DPO loss function increases the relative log-probabilities of preferred to un-preferred responses, whilst using log probabilities from a reference model to prevent policy degradation during training.

In this post, I aim to derive the DPO objective in a more detailed, step-by-step manner. We first expand the KL term to its expectation form and merge it with the expectation over responses in the reward term.