Unsupervised reasoning emerges through curriculum learning without manual annotation
Researchers propose Abstract Token Curriculum, a curriculum learning framework that trains language models to develop internal reasoning representations without explicit supervision or manual scratchpad annotation. Rather than requiring task-specific chain-of-thought data, ATC progressively increases problem complexity to elicit continuous intermediate 'thoughts' in the model's representation space. This addresses a practical bottleneck in scaling reasoning capabilities: the labor cost of annotating reasoning steps. The work combines theoretical grounding with empirical validation, positioning unsupervised reasoning development as an alternative to supervised chain-of-thought methods and potentially lowering the data requirements for building reasoning-capable models.
Modelwire context
ExplainerThe key omission from the summary: ATC doesn't just reduce annotation labor, it claims models develop reasoning representations that are interpretable and transferable across tasks. The paper's actual novelty hinges on whether these unsupervised intermediate 'thoughts' match the structure of supervised chain-of-thought reasoning or diverge entirely.
This connects directly to the interpretability thread from 'Generalization through Lexical Abstraction' (Sept 17), which probed whether transformers develop human-like compositional reasoning or rely on surface patterns. ATC takes that question further: if models can learn to reason without explicit supervision, it suggests reasoning structure emerges naturally from curriculum design rather than being imposed by annotated examples. The work also echoes 'PetriBench' (same date), which exposed reasoning gaps in structured domains. If ATC's unsupervised reasoning generalizes to formal systems like Petri nets, it would suggest the bottleneck was never reasoning capability itself but rather the cost of labeling it.
If the authors release ablations showing that ATC-trained models transfer to out-of-distribution reasoning tasks (e.g., trained on arithmetic, tested on logic puzzles) without retraining, that confirms the learned representations are genuinely task-agnostic. If transfer requires fine-tuning, the method is mainly a data efficiency win for supervised learning, not a fundamental shift in how models acquire reasoning.
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MentionsAbstract Token Curriculum · Chain-of-thought · Large Language Models
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