Anatomy of Post-Training: using interpretability to characterize data and shape the learning signal

arXiv·medium signal

This paper (arXiv 2606.12360, Bergen, Bhalla, Baskaran) argues post-training is the main stage where model behavior is shaped yet still relies on optimizing scalar rewards, and uses interpretability to characterize training data and reshape the learning signal more deliberately. The work points toward data-aware, interpretable post-training rather than blunt reward maximization. Relevant to teams fine-tuning or RL-tuning models that back their agents, where behavioral control during post-training directly determines downstream agent reliability.