LLMs Encode Their Failures: Predicting Success from Pre-Generation Activations
📰 ArXiv cs.AI
LLMs can predict their own success from pre-generation activations, enabling more efficient inference
Action Steps
- Train linear probes on pre-generation activations to predict policy-specific success
- Use the predicted success signal to guide more efficient inference
- Apply this approach to math and coding tasks to evaluate its effectiveness
- Investigate the generalizability of this method to other tasks and domains
Who Needs to Know This
AI engineers and researchers can benefit from this knowledge to optimize LLM performance and reduce computational costs, while ML researchers can apply these findings to improve model efficiency
Key Insight
💡 LLMs' internal representations before generation contain signals about their likelihood of success, which can be used to improve efficiency
Share This
💡 LLMs can predict own success from pre-gen activations, optimizing inference efficiency
Key Takeaways
LLMs can predict their own success from pre-generation activations, enabling more efficient inference
Full Article
Title: LLMs Encode Their Failures: Predicting Success from Pre-Generation Activations
Abstract:
arXiv:2602.09924v3 Announce Type: replace-cross Abstract: Running LLMs with extended reasoning on every problem is expensive, but determining which inputs actually require additional compute remains challenging. We investigate whether their own likelihood of success is recoverable from their internal representations before generation, and if this signal can guide more efficient inference. We train linear probes on pre-generation activations to predict policy-specific success on math and coding t
Abstract:
arXiv:2602.09924v3 Announce Type: replace-cross Abstract: Running LLMs with extended reasoning on every problem is expensive, but determining which inputs actually require additional compute remains challenging. We investigate whether their own likelihood of success is recoverable from their internal representations before generation, and if this signal can guide more efficient inference. We train linear probes on pre-generation activations to predict policy-specific success on math and coding t
DeepCamp AI