SimPO Loss

Overview

SimPO is a reference-free preference optimization algorithm. Its key innovation is using the average log probability of a sequence as the implicit reward, plus a target reward margin γ.

Loss Formula (Eq. 6 from the paper)

L_SimPO = -E log σ(β/|yw| · log πθ(yw|x) - β/|yl| · log πθ(yl|x) - γ)

Since the log probabilities passed to simpo_loss are already length-normalized (average log prob), the loss simplifies to:

logits = β * policy_chosen_logps - β * policy_rejected_logps - γ

where γ = gamma_beta_ratio * beta.

Loss Types

• sigmoid (default): losses = -log σ(logits) * (1 - label_smoothing) - log σ(-logits) * label_smoothing
• hinge: losses = relu(1 - logits)

Rewards

• chosen_rewards = β * policy_chosen_logps
• rejected_rewards = β * policy_rejected_logps

Default Hyperparameters

• β = 2.0
• gamma_beta_ratio = 0.25 (so γ = 0.5)
• label_smoothing = 0.0
• loss_type = "sigmoid"