Auditing Preference Biases and Fine-Tuning Language Models with Direct Preference Optimization on Anthropic HH-RLHF Using TRL and LoRA

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In this tutorial, we design an end-to-end preference-learning workflow using the Anthropic HH-RLHF dataset and Direct Preference Optimization (DPO). We begin by preparing a robust Colab environment, loading and parsing chosen–rejected response pairs, and auditing the dataset for structural and length-based preference biases. We then run lexical shortcut diagnostics to determine whether surface-level linguistic patterns can separate preferred from rejected responses, prepare conversational data with tokenizer-aware length filtering, and construct a version-robust DPO training pipeline with TRL and optional LoRA adaptation. Finally, we fine-tune a Qwen2.5-0.5B-Instruct model, evaluate reward accuracy and training behavior, analyze performance across individual HH-RLHF subsets, inspect potential length bias, generate sample responses, and save the resulting policy for further experimentation.

Copy CodeCopiedUse a different Browserimport dataclasses import importlib.util import inspect import os import re import subprocess import sys import warnings warnings.filterwarnings("ignore", category=UserWarning) REQUIRED = ["trl>=0.12", "transformers>=4.45", "accelerate", "datasets", "peft", "scikit-learn"] def ensure_deps(): """Install in ONE pip call so the resolver picks a mutually compatible set.""" try: import trl import transformers return False except ImportError: print("Installing dependencies...") subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "-U", *REQUIRED]) return True def drop_broken_torchao(): """Colab ships torchao 0.10.0; peft demands …

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