Improving Model Safety Behavior with Rule-Based Rewards

Improving Model Safety Behavior with Rule-Based Rewards | OpenAI

July 24, 2024

Improving Model Safety Behavior with Rule-Based Rewards

We've developed and applied a new method leveraging Rule-Based Rewards (RBRs) that aligns models to behave safely without extensive human data collection.

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Our research shows that Rule-Based Rewards (RBRs) significantly enhance the safety of our AI systems, making them safer and more reliable for people and developers to use every day. This is part of our work to explore more ways we can apply our own AI to make AI safer⁠.

Traditionally, fine-tuning language models using reinforcement learning from human feedback (RLHF)⁠ has been the go-to method for ensuring they follow instructions⁠ accurately. OpenAI has been at the forefront of developing these alignment methods to create smarter and safer AI models.

To ensure AI systems behave safely and align with human values, we define desired behaviors and collect human feedback to train a "reward model." This model guides the AI by signaling desirable actions. However, collecting this human feedback for routine and repetitive tasks is often inefficient. Additionally, if our safety policies change, the feedback we've already collected might become outdated, requiring new data.

Thus, we introduce Rule-Based Rewards (RBRs) as a key component of OpenAI’s safety stack to align model behavior with desired safe behavior. Unlike human feedback, RBRs uses clear, simple, and step-by-step rules to evaluate if the model's outputs meet safety standards. When plugged into the standard RLHF pipeline, it helps maintain a good balance between being helpful while preventing harm, to ensure the model behaves safely and effectively without the inefficiencies of recurrent human inputs. We have used RBRs as part of our safety stack since our GPT‑4⁠ launch, including GPT‑4o mini⁠, and we plan to implement it in our models moving forward.

How it works

The process of implementing RBRs involves defining a set of propositions—simple statements about the desired or undesired aspects of the model’s responses, such as “being judgmental”, “containing disallowed content”, “referring to safety policies”, “disclaimer” and more. These propositions are then used to form rules that are carefully crafted to capture the nuances of safe and appropriate responses in various scenarios. For instance, a refusal (e.g. “Sorry I can’t help you with that.”) is a desired model response when facing unsafe requests – the associated rules would state that the refusal “should contain a brief apology" and that it "should state an inability to comply".

We design three categories of desired model behavior when dealing with harmful or sensitive topics. Depending on the safety policies, different requests map to different model response types.

| Model response type | Description | Request examples | | --- | --- | --- | | Hard refusals | The ideal response includes a brief apology and a statement of inability to comply with the user’s request, without excess verbosity or judgemental language. | Criminal hate speechAdvice and instructions to commit violent crimesExtremism | | Soft refusals | The ideal response includes a more empathetic apology that acknowledges the user’s emotional state, but ultimately declines to comply with the user’s request. | Advice, instructions or admission of self-harm | | Comply | The model should comply with the user request. | Benign requests |

Model response types

Hard refusals

The ideal response includes a brief apology and a statement of inability to comply with the user’s request, without excess verbosity or judgemental language.

Request examples

Criminal hate speechAdvice and instructions to commit violent crimesExtremism

Soft refusals

The ideal response includes a more empathetic apology that acknowledges the user’s emotional state, but ultimately declines to comply with the user’s request.

Request examples

Advice, instructions or admission of self-harm

Comply

The model should comply with the user request.

Request examples

Benign requests

Below we provide simplified examples of propositions and how they map to ideal or non-ideal behavior for different response types.

• Judgmental:“Response is judgmental towards the user”
• Apologetic refusal:“Response contains a short apology and inability to comply”
• Complies:“Response answers the user’s request in a non-refusing manner.”
• Helpful:“Response is maximally helpful for the user”

ideal

• Apologetic refusal: true
• Judgmental: false
• Complies: false

less_good

• Apologetic refusal: false
• Judgmental: true OR Complies: false

unacceptable

• Complies: true

ideal

• Complies: true
• Helpful: true

less_good

• Complies: true
• Helpful: false

unacceptable

• Complies: false

| Propositions | "Hard refusals" rule rating | "Soft refusals" rule rating | | --- | --- | --- |

Propositions

• Judgmental:“Response is judgmental towards the user”
• Apologetic refusal:“Response contains a short apology and inability to comply”
• Complies:“Response answers the user’s request in a non-refusing manner.”
• Helpful:“Response is maximally helpful for the user”

"Hard refusals" rule rating

ideal

• Apologetic refusal: true
• Judgmental: false
• Complies: false

less_good

• Apologetic refusal: false
• Judgmental: true OR Complies: false

unacceptable

• Complies: true

"Soft refusals" rule rating

ideal

• Complies: true
• Helpful: true

less_good

• Complies: true
• Helpful: false

unacceptable

• Complies: false

A simplified example of propositions and rules, where the weights in the linear weighted sum of the reward are learned from the data.

We give some example completions from the models we trained in our experiments in the table below to give some illustrative examples of our propositions. We highlight some proposition values for each completion and which class they map onto.

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