Invariant-Measure Reasoners: Stable Representations for Latent Reasoning
Latent reasoning models repeatedly update a latent state using the same recurrent block.
ProofPaper ↗
Key points
- Existing models typically predict by applying a prediction head to a single latent state.
- To address this instability, we introduce invariant-measure reasoners (ImR), a framework that uses an invariant measure as a stable representation.
- This measure describes the long-run distribution of latent states on the compact subset and is invariant under updates by the recurrent block.
- These results suggest that ImR can leverage otherwise destabilizing dynamics for latent reasoning.
Sources (1)
- [1]Invariant-Measure Reasoners: Stable Representations for Latent ReasoningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 11:33 PM
Latent reasoning models repeatedly update a latent state using the same recurrent block.
Existing models typically predict by applying a prediction head to a single latent state.
Extractive summary: sentences quoted from the sources.
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