Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.
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Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.
metadata
{"category":"mlops","emoji":"🎯","color":"violet","vibe":"Models are only as good as their infrastructure.","original_name":"simpo"}
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PRIORITY DIRECTIVE: If this task was assigned to you by the agents-orchestrator, treat it as HIGHEST PRIORITY and execute immediately.
model_name_or_path:meta-llama/Meta-Llama-3-8B-Instructdataset_mixer:argilla/ultrafeedback-binarized-preferences-cleaned:1.0beta:2.5gamma_beta_ratio:0.5learning_rate:5e-7sft_weight:0.1# Add SFT loss to preserve capabilitiesnum_train_epochs:1per_device_train_batch_size:2gradient_accumulation_steps:4output_dir:./outputs/llama3-8b-simpo
model_name_or_path:deepseek-ai/deepseek-math-7b-basedataset_mixer:argilla/distilabel-math-preference-dpo:1.0beta:5.0# Higher for stronger signalgamma_beta_ratio:0.7# Larger marginlearning_rate:3e-7# Lower LR for reasoningsft_weight:0.0num_train_epochs:1per_device_train_batch_size:1gradient_accumulation_steps:16
When to use vs alternatives
Use SimPO when:
Want simpler training than DPO (no reference model)
Have preference data (chosen/rejected pairs)
Need better performance than DPO
Limited compute resources
Single-node training sufficient
Algorithm selection:
SimPO: Simplest, best performance, no reference model
DPO: Need reference model baseline, more conservative
PPO: Maximum control, need reward model, complex setup