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cloud.google.com·6h ago
AI Research
arXiv CS.AI·21d ago·5 min read
When Steering Fails in Latent Reasoning: A Latent-to-Language Transition Gap
TL;DR
Research reveals that steering language models during latent chain-of-thought reasoning is less effective than during explicit reasoning. This is due to a latent-to-language transition gap that weakens the influence of interventions in latent space on language generation.
✦ Why It Matters
Focus on improving latent-to-language transition techniques to enhance model steering effectiveness.
Key Takeaways
- Investigate the effectiveness of steering in latent reasoning tasks.
- Focus on the transition interface for future model improvements.
- Consider the impact of task-related directions in latent CoT.
- Evaluate steering methods based on their output distribution changes.
How It Works
Activation steering adjusts the internal representations of language models to influence reasoning processes. In explicit CoT, this steering effectively guides the model's output.
However, in latent CoT, the same adjustments yield weaker results due to the latent-to-language transition gap, where changes in latent space do not translate well into coherent language generation.