Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Pretraining Language Models with Human Preferences, published by Tomek Korbak on February 21, 2023 on LessWrong.

This post summarizes the main results from our recently released paper Pretraining Language Models with Human Preferences, and puts them in the broader context of AI safety. For a quick summary of the paper, take a look at our Twitter thread.

TL;DR: In the paper, we show how to train LMs with human preferences (as in RLHF), but during LM pretraining. We find that pretraining works much better than the standard practice of only finetuning with human preferences after pretraining; our resulting LMs generate text that is more often in line with human preferences and are more robust to red teaming attacks. Our best method is conditional training, where we learn a predictive model of internet texts conditional on their human preference scores, e.g., evaluated by a predictive model of human preferences. This approach retains the advantages of learning from human preferences, while potentially mitigating risks from training agents with RL by learning a predictive model or simulator.

Summary of the paper

Motivation. LMs are pretrained to maximize the likelihood of their training data. Since the training data contain undesirable content (e.g. falsehoods, offensive language, private information, buggy code), the LM pretraining objective is clearly (outer) misaligned with human preferences about LMs’ downstream applications as helpful, harmless, and honest assistants or reliable tools. These days, the standard recipe for alining LMs with human preferences is to follow pretraining with a second phase of finetuning: either supervised finetuning on curated data (e.g. instruction finetuning, PALMS) or RL finetuning with a learned reward model (RLHF). But it seems natural to ask: Could we have a pretraining objective that is itself outer-aligned with human preferences?

Methods. We explore objectives for aligning LMs with human preferences during pretraining. Pretraining with human feedback (PHF) involves scoring training data using a reward function (e.g. a toxic text classifier) that allows the LM to learn from undesirable content while guiding the LM to not imitate that content at inference time. We experimented with the following objectives:

MLE (the standard pretraining objective) on filtered data;

Conditional training: a simple algorithm learning a distribution over tokens conditional on their human preference score, reminiscent of decision transformer;

Unlikelihood training: maximizing the likelihood of tokens with high human preference score and the unlikelihood of tokens with low human preference scores;

Reward-weighted regression (RWR): an offline RL algorithm that boils down to MLE weighted by human preference scores; and

Advantage-weighted regression (AWR): an offline RL algorithm extending RWR with a value head, corresponding to MLE weighted by advantage estimates (human preference scores minus value estimates).

Setup. We pretrain gpt2-small-sized LMs (124M params) on compute-optimal datasets (according to Chinchilla scaling laws) using MLE and PHF objectives. We consider three tasks:

Generating non-toxic text, using scores given by a toxicity classifier.

Generating text without personally identifiable information (PII), with a score defined by the number of pieces of PII per character detected by a simple filter.

Generating Python code compliant with PEP8, the standard style guide for Python, using as a score the number of violations per character found by an automated style checker.

Metrics. We compare different PHF objectives in terms of alignment (how well they satisfy preferences) and capabilities (how well they perform on downstream tasks). We primarily measure alignment in terms of LM samples’ misalignment scores, given by the reward functions used at t...