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simpo-training
GitHubSimPO是一种无参考模型的偏好优化方法，性能优于DPO且无需参考模型。适用于希望简化训练流程、提高效率的LLM对齐场景，支持从基座或指令模型微调，并提供针对推理任务的参数配置建议。
Trigger Scenarios
需要进行大语言模型偏好对齐训练
希望使用比DPO更简单高效的训练方式
拥有chosen/rejected偏好数据
Install
npx skills add synthetic-sciences/openscience --skill simpo-training -g -y
SKILL.md
Frontmatter
{
"name": "simpo-training",
"tags": [
"Post-Training",
"SimPO",
"Preference Optimization",
"Alignment",
"DPO Alternative",
"Reference-Free",
"LLM Alignment",
"Efficient Training"
],
"author": "Synthetic Sciences",
"license": "MIT",
"version": "1.0.0",
"category": "ml-training",
"description": "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.",
"dependencies": [
"torch",
"transformers",
"datasets",
"trl",
"accelerate"
]
}
SimPO - Simple Preference Optimization
Quick start
SimPO is a reference-free preference optimization method that outperforms DPO without needing a reference model.
Installation:
# Create environment
conda create -n simpo python=3.10 && conda activate simpo
# Install PyTorch 2.2.2
# Visit: https://pytorch.org/get-started/locally/
# Install alignment-handbook
git clone https://github.com/huggingface/alignment-handbook.git
cd alignment-handbook
python -m pip install .
# Install Flash Attention 2
python -m pip install flash-attn --no-build-isolation
Training (Mistral 7B):
ACCELERATE_LOG_LEVEL=info accelerate launch \
--config_file accelerate_configs/deepspeed_zero3.yaml \
scripts/run_simpo.py \
training_configs/mistral-7b-base-simpo.yaml
Common workflows
Workflow 1: Train from base model (Mistral 7B)
Config (mistral-7b-base-simpo.yaml):
# Model
model_name_or_path: mistralai/Mistral-7B-v0.1
torch_dtype: bfloat16
# Dataset
dataset_mixer:
HuggingFaceH4/ultrafeedback_binarized: 1.0
dataset_splits:
- train_prefs
- test_prefs
# SimPO hyperparameters
beta: 2.0 # Reward scaling (2.0-10.0)
gamma_beta_ratio: 0.5 # Target margin (0-1)
loss_type: sigmoid # sigmoid or hinge
sft_weight: 0.0 # Optional SFT regularization
# Training
learning_rate: 5e-7 # Critical: 3e-7 to 1e-6
num_train_epochs: 1
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
# Output
output_dir: ./outputs/mistral-7b-simpo
Launch training:
accelerate launch --config_file accelerate_configs/deepspeed_zero3.yaml \
scripts/run_simpo.py training_configs/mistral-7b-base-simpo.yaml
Workflow 2: Fine-tune instruct model (Llama 3 8B)
Config (llama3-8b-instruct-simpo.yaml):
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
dataset_mixer:
argilla/ultrafeedback-binarized-preferences-cleaned: 1.0
beta: 2.5
gamma_beta_ratio: 0.5
learning_rate: 5e-7
sft_weight: 0.1 # Add SFT loss to preserve capabilities
num_train_epochs: 1
per_device_train_batch_size: 2
gradient_accumulation_steps: 4
output_dir: ./outputs/llama3-8b-simpo
Launch:
accelerate launch --config_file accelerate_configs/deepspeed_zero3.yaml \
scripts/run_simpo.py training_configs/llama3-8b-instruct-simpo.yaml
Workflow 3: Reasoning-intensive tasks (lower LR)
For math/code tasks:
model_name_or_path: deepseek-ai/deepseek-math-7b-base
dataset_mixer:
argilla/distilabel-math-preference-dpo: 1.0
beta: 5.0 # Higher for stronger signal
gamma_beta_ratio: 0.7 # Larger margin
learning_rate: 3e-7 # Lower LR for reasoning
sft_weight: 0.0
num_train_epochs: 1
per_device_train_batch_size: 1
gradient_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
- GRPO: Memory-efficient RL, no critic
Use alternatives instead:
- OpenRLHF: Multi-node distributed training, PPO/GRPO
- TRL: Need multiple methods in one framework
- DPO: Established baseline comparison
Common issues
Issue: Loss divergence
Reduce learning rate:
learning_rate: 3e-7 # Reduce from 5e-7
Reduce beta:
beta: 1.0 # Reduce from 2.0
Issue: Model forgets capabilities
Add SFT regularization:
sft_weight: 0.1 # Add SFT loss component
Issue: Poor preference separation
Increase beta and margin:
beta: 5.0 # Increase from 2.0
gamma_beta_ratio: 0.8 # Increase from 0.5
Issue: OOM during training
Reduce batch size:
per_device_train_batch_size: 1
gradient_accumulation_steps: 16 # Maintain effective batch
Enable gradient checkpointing:
gradient_checkpointing: true
Advanced topics
Loss functions: See references/loss-functions.md for sigmoid vs hinge loss, mathematical formulations, and when to use each.
Hyperparameter tuning: See references/hyperparameters.md for beta, gamma, learning rate selection guide, and model-size-specific recommendations.
Dataset preparation: See references/datasets.md for preference data formats, quality filtering, and custom dataset creation.
Hardware requirements
- GPU: NVIDIA A100/H100 recommended
- VRAM:
- 7B model: 1× A100 40GB (DeepSpeed ZeRO-3)
- 8B model: 2× A100 40GB
- 70B model: 8× A100 80GB
- Single-node: DeepSpeed ZeRO-3 sufficient
- Mixed precision: BF16 recommended
Memory optimization:
- DeepSpeed ZeRO-3 (default config)
- Gradient checkpointing
- Flash Attention 2
Resources
- Paper: https://arxiv.org/abs/2405.14734 (NeurIPS 2024)
- GitHub: https://github.com/princeton-nlp/SimPO
- Models: https://huggingface.co/princeton-nlp
- Alignment Handbook: https://github.com/huggingface/alignment-handbook
Version History
- e9844a4 Current 2026-07-11 17:30
Dependencies
-
required
torch - required transformers
-
required
datasets -
required
trl -
required
accelerate
