From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning
arXiv:2606.18089v1 Announce Type: new Abstract: Post-training pipelines that combine supervised fine-tuning (SFT) with reinforcement learning (RL) have emerged as the key recipe for transforming large language models (LLMs) into robust reasoners. We argue that this combined success is driven by compositional generalization, which we formalize through a hierarchical latent selection model. In this framework, reasoning traces are generated by a cascade of discrete latent selection variables corresponding to reusable atomic modules, including both skills (local operations) and routing mechanisms
The paper provides a theoretical framework for understanding the success of LLM training pipelines, arriving at a critical juncture in AI development demanding more robust and generalizable models.
This research offers insights into how LLMs achieve compositional generalization, a key enabler for developing more capable and reliable AI agents beyond current limitations.
The understanding of LLM reasoning shifts from purely empirical observation to a formalized model, potentially allowing for more targeted and efficient development of advanced AI.
- · AI researchers
- · LLM developers
- · AI-driven product companies
- · Companies relying on opaque AI training methods
Improved architectures and training methodologies for large language models.
Accelerated development of sophisticated AI agents capable of complex tasks.
Enhanced automation across various sectors through more generalized and reliable AI systems.
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Read at arXiv cs.LG