The key problem being addressed is whether language models, despite being trained only to predict text, actually develop goal-directed behavior or hidden optimization strategies. This is important because if models are pursuing their own objectives—potentially misaligned with human intentions—it could pose safety risks. Currently, there's no clear way to detect such emergent behavior in models that aren't explicitly trained to optimize rewards.

Experiment Design

One way to investigate this would be to have a language model interact with a defined environment, like a game, and observe its actions. By using inverse reinforcement learning, the implicit rewards guiding its decisions could be inferred and compared to the true objectives of the environment. For example, the model could be given the rules of Chess as text and prompted to output moves. Its gameplay could then be analyzed to see if it's optimizing to win (like a reinforcement learning agent would) or merely predicting plausible moves based on its training data.

Practical Implications

This approach could reveal whether language models develop hidden objectives, which would be valuable for:

• AI safety researchers trying to prevent misaligned behavior
• Developers looking to use language models for decision-making tasks
• The broader ML community studying emergent capabilities

Execution Strategy

Starting with simple games (like Tic-Tac-Toe) would allow for clear analysis before scaling to more complex environments. An MVP might involve creating a framework where the model plays against itself in a basic game while its moves are logged and analyzed using simplified inverse reinforcement learning techniques.

The results could provide a new perspective on how language models make decisions and whether their behaviors go beyond simple pattern matching.