“Model organisms researchers should check whether high LRs defeat their model organisms” by dx26, Sebastian Prasanna, Alek Westover, Vivek Hebbar, Julian Stastny

Thanks to Buck Shlegeris for feedback on a draft of this post. The goal-guarding hypothesis states that schemers will be able to preserve their goals during training by taking actions which are selected for by the training process. To investigate the goal-guarding hypothesis, we’ve been running experiments of the following form: We call this type of training “behavior-compatible training”. This type of experiment is common in model organism (MO) research. For example, Sleeper Agents is of this form: One motivation for studying behavior-compatible training is to understand whether transfer will remove sandbagging, i.e., to understand how the following setup goes: More generally, understanding when behavior-compatible training removes the initial behavior seems somewhat relevant to understanding when schemers will be able to successfully goal-guard (although, there are important differences between these questions: for example with goal-guarding we are dealing with an intelligent adversary that might actively try to prevent transfer). It's also relevant for understanding the validity of claims about how training-resistant MOs are. The purpose of this brief research note is to highlight a surprising-to-us phenomenon: Sometimes behavior-compatible SFT is much more compute-efficient at causing transfer at high LR. More precisely, sometimes if behavior-compatible SFT at high LR removes [...] ---Outline:(03:12) Experimental setup(04:46) Results(07:22) Capability degradation(09:25) Conclusion(09:48) Appendix(09:51) Appendix 1: More settings(10:12) Appendix 2: Convergence error bars justification --- First published: April 9th, 2026 Source: https://www.lesswrong.com/posts/vKR9rHcWsMMr7BX4Q/model-organisms-researchers-should-check-whether-high-lrs --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.