Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal
arXiv:2606.12360v1 Announce Type: new Abstract: Language-model post-training is the main stage at which model behavior is shaped, yet it still largely involves optimization of scalar rewards that summarize diverse desiderata. This abstraction gives practitioners little visibility into what their data actually teaches models, allowing spurious correlations to be learned by a model and inducing undesirable behaviors such as over-stylization and sycophancy. To address this problem, we ask: can we inspect a preference dataset before optimization and decide, at the level of concepts, which behavior
The increasing sophistication of language models and the critical need for more reliable and less 'sycophantic' AI behavior necessitates advanced interpretability methods for post-training processes.
Improving the interpretability of post-training data directly addresses issues of AI alignment, safety, and trustworthiness, which are paramount for the broader adoption and beneficial integration of AI systems.
The ability to inspect and characterize preference datasets before optimization means practitioners can proactively mitigate undesirable AI behaviors, leading to more robust and ethical AI development.
- · AI developers
- · AI ethics researchers
- · Enterprises deploying AI
- · AI governance bodies
- · Developers relying on black-box optimization
- · AI systems prone to bias or sycophancy
Researchers will gain better insight into how training data shapes AI model behavior.
This improved understanding will lead to the development of more aligned and trustworthy AI models across various applications.
The enhanced interpretability and control over AI behavior could accelerate the deployment of autonomous AI agents in sensitive domains.
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Read at arXiv cs.LG