Define a simple reward model (binary classification) [chapter] deterministic

reward_model = AutoModelForSequenceClassification.from_pretrained( model_name, num_labels=2, ignore_mismatched_sizes=True ) training_args = TrainingArguments( output_dir="./rlhf_results", per_device_train_batch_size=8, learning_rate=5e-6, num_train_epochs=3, save_strategy="epoch", logging_steps=10, ) trainer = Trainer( model=reward_model, args=training_args, train_dataset=dataset, ) trainer.train()

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This code trains a reward model that distinguishes between “chosen” (preferred) and “rejected” (dispreferred) outputs. Once trained, you can use the reward model to guide fine-tuning of the base language model via PPO or DPO. For local-first systems, you would run this training on a GPU-equipped machine or, if resources are limited, use quantized models to reduce memory footprint.

Quality Control for AI Training Data Quality control is essential to ensure that annotated data is reliable. Key strategies include: - **Inter-annotator agreement:** Compute statistical measures such as Cohen’s kappa or Fleiss’ kappa to quantify consistency among annotators. - **Automated checks:** Use rule-based validators to flag impossible label combinations (e.g., a sentiment label of “positive” on a clearly negative sentence). - **Active learning:** Prioritize data points that the model is uncertain about, ensuring that annotations focus on high-impact examples. - **Continuous improvement:** Treat the annotation pipeline as a learning that evolves over time. This aligns with the concept of **Lifelong Learning (Voyager)**, which emphasizes adaptability and self-improvement in autonomous architectures. By continuously refining labeling guidelines and updating the annotation platform, you keep the data fresh and relevant. The following code snippet demonstrates how to compute Cohen’s kappa for a binary labeling task using the sklearn library.

```python