Workflow:CarperAI Trlx RLHF Dialogue Alignment
| Knowledge Sources | |
|---|---|
| Domains | LLMs, RLHF, Dialogue, Alignment |
| Last Updated | 2026-02-07 16:00 GMT |
Overview
End-to-end pipeline for aligning a dialogue language model on the Anthropic Helpful-Harmless (HH) dataset using SFT, ILQL, and PPO training methods in trlX.
Description
This workflow demonstrates how to align a dialogue model to be helpful and harmless using the Anthropic HH-RLHF dataset. It provides three complementary training approaches through the trlX framework: supervised fine-tuning (SFT) on chosen dialogue responses, offline RL via ILQL on preference-labeled dialogue pairs, and online RL via PPO with a trained reward model. The workflow supports multiple model scales from 125M to 20B parameters, with per-scale configuration presets. A shared reward model infrastructure (supporting both local GPU inference and Triton server deployment) provides reward signals for both PPO training and evaluation metrics.
Usage
Execute this workflow when you want to train a language model to produce helpful, harmless, and honest dialogue responses. The dataset contains multi-turn Human/Assistant conversations with chosen and rejected completions. This workflow is appropriate for training chatbot or assistant models and demonstrates how different RLHF approaches compare on the same alignment task.
Execution Steps
Step 1: Prepare the HH-RLHF dataset
Load the Anthropic HH-RLHF dataset, which contains pairs of multi-turn dialogues with chosen (preferred) and rejected responses. Preprocess the data according to the training method: for SFT, extract chosen completions; for ILQL, create prompt-completion pairs with +1/-1 reward labels; for PPO, extract prompts with metadata for delta-reward computation.
Key considerations:
- The dataset uses "Human:" and "Assistant:" turn markers
- For SFT: concatenate prompt and chosen response into a single training sample
- For ILQL: create two samples per example (chosen=+1 reward, rejected=-1 reward)
- For PPO: extract prompts and store original chosen outputs for delta-reward baseline
- Stop sequences ("Human:", "Assistant:") prevent generation from continuing past the expected response boundary
Step 2: Set up the reward model
Initialize or load a pre-trained reward model that scores dialogue quality. The reward infrastructure supports two deployment modes: a local GPU-based GPT-J reward model (suitable for single-node training) and a Triton Inference Server deployment (for scalable multi-node training). The reward model is used during PPO training as the reward function and during SFT/ILQL evaluation as the metric function.
Key considerations:
- Local mode loads a GPT-J-6B reward model with a value head on the last available GPU
- Triton mode connects to an inference server for scalable reward computation
- Delta-reward (improvement over original response) is used for PPO training stability
- On non-zero ranks in distributed training, a dummy reward function signals the trainer to wait for rank 0 scores
Step 3: Configure training method
Select one of three training approaches and configure the corresponding trlX settings. Each method uses a different trainer class and method-specific configuration. Model scale (125M, 1B, 6B, 20B) is selected via an environment variable that adjusts batch sizes, learning rates, and checkpoint paths automatically.
Training approaches:
- SFT (AccelerateSFTTrainer): Supervised learning on chosen responses only. Simplest but relies entirely on data quality.
- ILQL (AccelerateILQLTrainer): Offline RL with Q-learning on preference pairs. Uses both chosen and rejected data with reward labels. More data-efficient than SFT.
- PPO (AcceleratePPOTrainer): Online RL with reward model scoring. Generates new responses and optimizes them. Most complex but can exceed the quality ceiling of the training data.
Key considerations:
- Larger models require smaller batch sizes and lower learning rates
- PPO uses 2 unfrozen layers by default for memory efficiency at large scale
- ILQL unfreezes all layers and uses double Q-learning (two_qs=True)
- The sequence length is 1024 tokens for all methods
Step 4: Launch training
Call trlx.train() with the prepared data, reward/metric functions, configuration, and stop sequences. The selected trainer handles the entire training loop: data loading, model initialization, optimization, periodic evaluation, logging, and checkpointing. Distributed training is managed by HuggingFace Accelerate with optional DeepSpeed integration.
Key considerations:
- PPO training requires the reward model to be loaded before calling trlx.train()
- Stop sequences ensure generated dialogue stays within the single-turn format
- All three methods log to Weights & Biases for experiment tracking
- Checkpoints are saved at configurable intervals for resumption and evaluation
Step 5: Evaluate model alignment
After training, evaluate the aligned model by generating dialogue responses and scoring them with the reward model. Compare reward scores across training methods (SFT vs. ILQL vs. PPO) and model scales to assess alignment quality. The evaluation uses the test split of the HH dataset.
Key considerations:
- PPO typically achieves the highest reward scores but requires more compute
- ILQL provides a good reward/compute trade-off for offline data
- SFT serves as the baseline and warm-start for the other methods
- Qualitative evaluation of generated dialogues is recommended alongside reward metrics