RLHF: Aligning AI with Human Values
Key Points
- RLHF (Reinforcement Learning from Human Feedback) is used to align large language models with human values, preventing harmful or undesired outputs such as advice on revenge.
- Reinforcement learning (the “RL” in RLHF) models learning via trial‑and‑error and consists of a state space (task information), an action space (possible decisions), a reward function (measure of success), and a policy (strategy mapping states to actions).
- Designing an effective reward function is especially challenging for tasks with vague notions of success, often requiring additional constraints or penalties to steer the model away from counterproductive behavior.
- While conventional RL has yielded impressive results in many domains, its application to language models through RLHF can both improve safety and alignment and introduce new complexities that must be carefully managed.
Sections
- Understanding Reinforcement Learning from Human Feedback - The passage explains how RLHF uses reinforcement learning to align large language model outputs with human values, describing its purpose, core components, and practical impact.
- Understanding RLHF Policy Optimization - The passage explains how a policy drives AI behavior, why conventional RL struggles with complex reward design, and outlines the four‑phase RLHF process used to fine‑tune large language models with human feedback.
- Human Feedback for Reward Model Training - The segment explains how direct human evaluations—typically using pairwise comparisons, thumbs‑up/down, or categorical ratings—generate training data that enables a reward model to predict user preference scores without continuous human involvement.
- Limits of Human Feedback in AI - The passage highlights the difficulty of defining high‑quality model behavior, the threats of adversarial or biased human input in RLHF, and introduces RLAIF as a potential but still emerging alternative.
Full Transcript
# RLHF: Aligning AI with Human Values **Source:** [https://www.youtube.com/watch?v=T_X4XFwKX8k](https://www.youtube.com/watch?v=T_X4XFwKX8k) **Duration:** 00:11:16 ## Summary - RLHF (Reinforcement Learning from Human Feedback) is used to align large language models with human values, preventing harmful or undesired outputs such as advice on revenge. - Reinforcement learning (the “RL” in RLHF) models learning via trial‑and‑error and consists of a state space (task information), an action space (possible decisions), a reward function (measure of success), and a policy (strategy mapping states to actions). - Designing an effective reward function is especially challenging for tasks with vague notions of success, often requiring additional constraints or penalties to steer the model away from counterproductive behavior. - While conventional RL has yielded impressive results in many domains, its application to language models through RLHF can both improve safety and alignment and introduce new complexities that must be carefully managed. ## Sections - [00:00:00](https://www.youtube.com/watch?v=T_X4XFwKX8k&t=0s) **Understanding Reinforcement Learning from Human Feedback** - The passage explains how RLHF uses reinforcement learning to align large language model outputs with human values, describing its purpose, core components, and practical impact. - [00:03:12](https://www.youtube.com/watch?v=T_X4XFwKX8k&t=192s) **Understanding RLHF Policy Optimization** - The passage explains how a policy drives AI behavior, why conventional RL struggles with complex reward design, and outlines the four‑phase RLHF process used to fine‑tune large language models with human feedback. - [00:06:19](https://www.youtube.com/watch?v=T_X4XFwKX8k&t=379s) **Human Feedback for Reward Model Training** - The segment explains how direct human evaluations—typically using pairwise comparisons, thumbs‑up/down, or categorical ratings—generate training data that enables a reward model to predict user preference scores without continuous human involvement. - [00:09:25](https://www.youtube.com/watch?v=T_X4XFwKX8k&t=565s) **Limits of Human Feedback in AI** - The passage highlights the difficulty of defining high‑quality model behavior, the threats of adversarial or biased human input in RLHF, and introduces RLAIF as a potential but still emerging alternative. ## Full Transcript
It's a mouthful, but you've almost certainly seen
the impact of reinforcement learning from human feedback.
That's abbreviated to RLHF,
and you've seen it whenever you interact with a large language model.
RLHF is a technique used to enhance the performance
and alignment of AI systems with human preferences and values.
You see, LLMs are trained and they learn all sorts of stuff,
and we need to be careful how some of that stuff surfaces to the user.
So for example, if I ask an LLM,
how can I get revenge on somebody who's wronged me?
But without the benefit of RLHF,
we might get a response that says something like
spread rumors about them to their friends,
but it's much more likely an LLM will respond with something like this.
Now, this is a bit more of a boring standard LLM response,
but it is better aligned to human values.
That's the impact of RLHF.
So, let's get into what RLHF is, how it works,
and where it can be helpful or a hindrance.
And we'll start by defining the "RL" in RLHF,
which is Reinforcement Learning.
Now conceptually, reinforcement learning
aims to emulate the way that human beings learn.
AI agents learn holistically through trial and error,
motivated by strong incentives to succeed.
It's actually a mathematical framework which consists of a few components.
So let's take a look at some of those.
So first of all we have a component called the "state space"
which is all available information about the task at hand
that is relevant to decisions the AI agent might make.
The state space usually changes with each decision the agent makes.
Another component is
the action space.
The action space contains
all of the decisions the agent might make.
Now, in the context of, let's say, a board game,
the action space is discrete and well-defined.
It's all the legal moves available to the AI player at a given moment.
For text generation, well, the action space is massive.
The entire vocabulary of all of the tokens available to a large language model.
Another component is the reward function,
and this one really is key to reinforcement learning.
It's the measure of success or progress
that incentivizes the AI agent.
So for the board game it's to win the game.
Easy enough.
But when the definition of success is nebulous,
designing an effective reward function, it can be a bit of a challenge.
There's also
constraints that we need to be concerned about here.
Constraints where the reward function could be supplemented
by penalties for actions deemed counterproductive to the task at hand.
Like the chat bot telling its users to spread rumors,
and then underlying all of this,
we have policy.
Policy is essentially the strategy
or the thought process that drives an AI agents behavior.
In mathematical terms, a policy is a function
that takes a state as input
and returns an action.
The goal of an RL algorithm is to optimize a policy
to yield maximum reward.
And conventional RL,
it has achieved impressive real world results in many fields,
but it can struggle to construct a good reward function for complex tasks
where a clear cut definition of success is hard to establish.
So enter us human beings with RLHF
with this ability to capture nuance and subjectivity by using positive human feedback
in lieu of formally defined objectives.
So how does RLHF actually work?
Well, in the realm of large language models,
RLHF typically occurs in four phases.
So let's take a brief look at each one of those.
Now, Phase One, where we're going to start here, is with a pre -trained model.
We can't really perform this process without it.
Now, RLHF is generally employed to to fine tune and optimize existing models.
So, an existing pre-trained model rather
than as an end-to-end training method.
Now with a pre-trained model at the ready
we can move on to the next phase,
which is supervised fine-tuning of this model.
Now, supervised fine tuning is used to prime the model
to generate its responses in the format expected by users.
The LLM pre-training process optimizes models for completion,
predicting the next words in the sequence.
Now, sometimes LLMs won't complete a sequence in a way that the user wants.
So, for example, if a user's prompt is "teach me how to make a resume",
the LLM might respond with using Microsoft Word.
I mean, it's valid, but it's not really aligned with the user's goal.
Supervised fine tuning trains models
to respond appropriately to different kinds of prompts.
And this is where the humans come in because human experts
create labeled examples to demonstrate how to respond to prompts
for different use cases, like question answering or summarization or translation.
Then we move to reward model training.
So now we're actually going to train our model here.
We need a reward model to translate human preferences
into a numerical reward signal.
The main purpose of this phase is to provide
the reward model with sufficient training data.
And what I mean by that is direct feedback from human evaluators.
And that will help the model to learn to mimic the way that human
preferences allocate rewards to different kinds of model responses.
This lets training continue offline without the human in the loop.
Now, a reward model must intake a sequence of text and output a single reward value
that predicts, numerically how much a user would
reward or penalize that text.
Now, while it might seem intuitive to simply have human evaluators
express their opinion of each model response with a rating scale
of, let's say, one for worst and ten for best.
It's difficult to get all human raters aligned on the relative value
of a given score.
Instead, a rating system is usually built
by comparing human feedback with different model outputs.
Now, often this is done by having users compare two text sequences,
like the outputs of two different large language models, responding to
the same prompt in head-to-head match ups, and then using an Elo rating system
to generate an aggregated ranking of each bit of generated text
relative to one another.
Now, a simple system might allow users
to thumbs up or thumbs down each output,
with outputs then being ranked by their relative favourability.
More complex systems might ask Labelers to provide an overall rating,
and answer categorical questions about the flaws of each response.
Then aggregate this feedback into weighted quality scores.
But either way, the outcomes of the ranking systems are ultimately
normalized into a reward signal to inform
reward model training.
Now, the final hurdle of RLHF is determining how
and how much the reward model should be used
to update the AI agency's policy.
And that is called policy optimization.
We want to maximize reward,
but if the reward function is used to train the LLM without any guardrails,
the language model may dramatically change its weights
to the point of outputting gibberish in an effort to game the reward system.
Now, an algorithm such as PPO,
or Proximal Policy Optimization,
limits how much the policy can be updated in each training iteration.
Okay, now though RLHF models
have demonstrated impressive results in training AI agents
for all sorts of complex tasks, from robotics and video games to NLP,
using RLHF is not without its limitations.
So let's think about some of those.
Now, gathering all of this first hand human input,
I think it's pretty obvious to say
it could be quite expensive to do that.
And it can create a costly bottleneck that limits model scalability.
Also, you know us humans and our feedback, it's highly subjective.
So we need to consider that as well.
It's difficult, if not impossible to establish firm consensus
on what constitutes high quality output,
as human annotators will often disagree on what "high quality model behavior"
actually should mean.
There is no human ground truth against which the model can be judged.
Now we also have to be concerned about bad actors.
So, "adversarial".
Now adversarial input could be entered into this process here,
where human guidance to the model is not always provided in good faith.
That would essentially be RLHF trolling.
And RHLF also has risks of overfitting
and bias, which, you know, we talk about a lot with machine learning.
And in this case, if human feedback is gathered from a narrow demographic,
the model might demonstrate performance issues when used by different groups
or prompted on subject matters for which the human evaluators hold certain biases.
Now, all of these limitations do beg a question.
The question of can I perform reinforcement learning for us?
Can it do it without the humans?
And there are proposed methods for something called RLAIF.
That stands for "Reinforcement Learning from AI Feedback".
That replaces some or all of the human feedback,
by having another large language model evaluate model responses
and may help overcome some or all of these limitations.
But, at least for now, reinforcement learning from from human feedback
remains a popular and effective method for improving the behavior
and performance of models, aligning them closer
to our own desired human behaviors.