New Paradigm: Scaling Inherently Interpretable Language Models Without Capability Tax
guidelabs · hf · 2026-08-11
Traditional LLMs are typically trained as opaque black boxes and explained post-hoc. This work challenges that premise by making interpretability a direct constraint within the training pipeline, optimized alongside the language modeling objective.
The authors instantiate this with Steerling-8B, a diffusion language model with a causal attention mask. Across three orders of magnitude of compute, interpretability scales with capability rather than against it. Surprisingly, model representations become more disentangled and aligned with human-understandable concepts at scale.
Steerling-8B attributes its outputs to input tokens, human concepts, and training data. This enables closed-loop intervention—diagnosing outputs, retrieving similar training data, and correcting behavior via concept steering without retraining. It remains competitive with open peer models trained on 2-16x more compute.
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