The authors replicate the two-step, grid world, and graph learning analyses on Gemma-2-27B and Qwen-2.5-72B. For both models, SAE features show strong correlations with TD-driven value and error signals that outperform alternative models (myopic, transition matrix, surprise). Behavioral patterns are qualitatively similar, though neither model performs the two-step task as well as Llama 3 70B, so RL model fits are less accurate. In the graph task, both models show strong SR correlations and community structures emerging over transformer blocks.
Evidence
correlational
Caveat
Neither model can do the two-step task as well as Llama, so RL models predict behavior less accurately; no interventional (lesioning) experiments were performed on these models.