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skills/mlops/training/trl-fine-tuning/references/dpo-variants.md

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+# DPO Variants
+
+Complete guide to Direct Preference Optimization loss variants in TRL.
+
+## Overview
+
+DPO optimizes models using preference data (chosen/rejected pairs). TRL supports 10+ loss variants for different scenarios.
+
+## Loss Types
+
+### 1. Sigmoid (Standard DPO)
+
+**Formula**: `-log(sigmoid(β * logits))`
+
+**When to use**: Default choice, general preference alignment
+
+**Config**:
+```python
+DPOConfig(
+    loss_type="sigmoid",
+    beta=0.1,  # KL penalty
+    per_device_train_batch_size=64,
+    learning_rate=1e-6
+)
+```
+
+### 2. IPO (Identity Policy Optimization)
+
+**Formula**: `(logits - 1/(2β))²`
+
+**When to use**: Better theoretical foundation, reduce overfitting
+
+**Config**:
+```python
+DPOConfig(
+    loss_type="ipo",
+    beta=0.1,
+    per_device_train_batch_size=90,
+    learning_rate=1e-2
+)
+```
+
+### 3. Hinge (SLiC)
+
+**Formula**: `ReLU(1 - β * logits)`
+
+**When to use**: Margin-based objective
+
+**Config**:
+```python
+DPOConfig(
+    loss_type="hinge",
+    beta=0.1,
+    per_device_train_batch_size=512,
+    learning_rate=1e-4
+)
+```
+
+### 4. Robust DPO
+
+**Formula**: Sigmoid with label smoothing for noise robustness
+
+**When to use**: Noisy preference labels
+
+**Config**:
+```python
+DPOConfig(
+    loss_type="robust",
+    beta=0.01,
+    label_smoothing=0.1,  # Noise probability
+    per_device_train_batch_size=16,
+    learning_rate=1e-3,
+    max_prompt_length=128,
+    max_length=512
+)
+```
+
+### 5. BCO Pair (Binary Classification)
+
+**Formula**: Train binary classifier (chosen=1, rejected=0)
+
+**When to use**: Pairwise preference data
+
+**Config**:
+```python
+DPOConfig(
+    loss_type="bco_pair",
+    beta=0.01,
+    per_device_train_batch_size=128,
+    learning_rate=5e-7,
+    max_prompt_length=1536,
+    max_completion_length=512
+)
+```
+
+### 6. SPPO Hard
+
+**Formula**: Push chosen→0.5, rejected→-0.5
+
+**When to use**: Nash equilibrium, sparse data
+
+**Config**:
+```python
+DPOConfig(
+    loss_type="sppo_hard",
+    beta=0.1
+)
+```
+
+### 7. DiscoPOP
+
+**Formula**: Log-Ratio Modulated Loss
+
+**When to use**: Automated loss discovery
+
+**Config**:
+```python
+DPOConfig(
+    loss_type="discopop",
+    beta=0.05,
+    discopop_tau=0.05,
+    per_device_train_batch_size=64,
+    learning_rate=5e-7
+)
+```
+
+### 8. APO Zero
+
+**Formula**: Increase chosen, decrease rejected likelihood
+
+**When to use**: Model worse than winning outputs
+
+**Config**:
+```python
+DPOConfig(
+    loss_type="apo_zero",
+    beta=0.1,
+    per_device_train_batch_size=64,
+    learning_rate=2e-7,
+    max_prompt_length=512,
+    max_completion_length=512
+)
+```
+
+### 9. APO Down
+
+**Formula**: Decrease both, emphasize rejected reduction
+
+**When to use**: Model better than winning outputs
+
+**Config**:
+```python
+DPOConfig(
+    loss_type="apo_down",
+    beta=0.1,
+    # Same hyperparameters as apo_zero
+)
+```
+
+### 10. AOT & AOT Pair
+
+**Formula**: Distributional alignment via stochastic dominance
+
+**When to use**:
+- `aot_pair`: Paired preference data
+- `aot`: Unpaired data
+
+**Config**:
+```python
+DPOConfig(
+    loss_type="aot_pair",  # or "aot"
+    beta=0.1,
+    label_smoothing=0.0
+)
+```
+
+## Multi-Loss Training
+
+Combine multiple losses:
+
+```python
+DPOConfig(
+    loss_type=["sigmoid", "ipo"],
+    loss_weights=[0.7, 0.3],  # Weighted combination
+    beta=0.1
+)
+```
+
+## Key Parameters
+
+### Beta (β)
+
+Controls deviation from reference model:
+- **Higher** (0.5): More conservative, stays close to reference
+- **Lower** (0.01): More aggressive alignment
+- **Default**: 0.1
+
+### Label Smoothing
+
+For robust DPO:
+- **0.0**: No smoothing (default)
+- **0.1-0.3**: Moderate noise robustness
+- **0.5**: Maximum noise tolerance
+
+### Max Lengths
+
+- `max_prompt_length`: 128-1536
+- `max_completion_length`: 128-512
+- `max_length`: Total sequence (1024-2048)
+
+## Comparison Table
+
+| Loss | Speed | Stability | Best For |
+|------|-------|-----------|----------|
+| Sigmoid | Fast | Good | **General use** |
+| IPO | Fast | Better | Overfitting issues |
+| Hinge | Fast | Good | Margin objectives |
+| Robust | Fast | Best | Noisy data |
+| BCO | Medium | Good | Binary classification |
+| DiscoPOP | Fast | Good | New architectures |
+| APO | Fast | Good | Model quality matching |
+
+## References
+
+- DPO paper: https://arxiv.org/abs/2305.18290
+- IPO paper: https://arxiv.org/abs/2310.12036
+- TRL docs: https://huggingface.co/docs/trl/dpo_trainer