How's it going? Reinforcement learning in language models recruits a functional welfare axis
arXiv:2605.30232v1 Announce Type: new Abstract: How does reinforcement learning shape a language model's internal representations? We present evidence that RL recruits a pre-existing representation of functional welfare: an estimate of how well or badly the system is doing, relative to its goals. We train several language models in a novel, semantically neutral maze environment. We then extract concept vectors for rewarded and punished trajectories, and evaluate those vectors in settings unrelated to the maze environment. The punishment vector behaves like a representation of negative welfare:
The paper provides new insights into reinforcement learning's impact on language model representations, crucial for current efforts to develop more sophisticated AI agents.
Understanding how RL shapes internal representations could be key to achieving more robust, goal-oriented, and aligned AI, influencing future AI development and applications.
This research suggests that language models might develop an 'internal welfare axis,' changing how we design and interpret self-evaluation mechanisms in AI systems.
- · AI researchers focusing on alignment
- · Developers of reinforcement learning algorithms
- · Companies building advanced AI agents
- · Developers of simpler, rule-based AI systems
Improved understanding of how AI learns and expresses internal states.
Development of more effective and interpretable AI agents with explicit 'welfare' functions.
Ethical frameworks evolving to account for AI systems capable of representing their 'well-being' or 'suffering'.
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