TL;DR: We find agents trained with RLAIF to indulge in wireheading in NetHack. Misalignment appears when the agent optimizes a combination of two rewards that produce aligned behaviors when optimized in isolation, and only emerges with some prompt wordings.

This post discusses an alignment-related discovery from our paper Motif: Intrinsic Motivation from Artificial Intelligence Feedback, co-led by myself (Pierluca D’Oro) and Martin Klissarov. If you’re curious about the full context in which the phenomenon was investigated, we encourage you to read the paper or the Twitter thread.

Our team recently developed Motif, a method to distill common sense from a Large Language Model (Llama 2 in our case) into NetHack-playing AI agents. Motif is based on reinforcement learning from AI feedback: it elicits the feedback of the language model on pairs of game messages (i.e., event captions), condenses that feedback into a reward function and then it gives it to an agent to play the game.

NetHack is a pretty interesting domain to study reinforcement learning from AI feedback: the game is remarkably complex in terms of required knowledge and strategies, offering a large surface area for AI agents to exploit any general capabilities they might obtain from a language model’s feedback.

We found that agents that optimize Motif’s intrinsic reward function exhibit behaviors that are quite aligned with human’s intuitions on how to play NetHack: they are attracted to explorative actions and play quite conservatively, getting experience and only venturing deeper into the game when it is safe. This is more human-aligned than the behaviors exhibited by agents trained to maximize the game score, which usually have a strong incentive to just go down the levels as much as they can.

When we compose Motif’s intrinsic reward with one that specifies a goal (by summing them), the resulting agent is able to succeed at tasks that had no reported progress without any expert demonstrations. One of these tasks is the oracle task, part of the suite from NetHack Learning Environment. The agent is asked to get near a character named “the oracle”, which typically appears in later levels of the game, that can only be reached with significant exploration and survival efforts.

In summary, this is what we observed about the performance in the oracle task:

Extrinsic-only: an agent trained with the ta

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