Entropy-Gradient Inversion: Moving Toward Internal Mechanism of Large Reasoning Models

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, short

Summary

The paper "Entropy-Gradient Inversion: Moving Toward Internal Mechanism of Large Reasoning Models" identifies and formally defines Entropy-Gradient Inversion, a robust negative correlation between token entropy and logit gradients. This phenomenon serves as a definitive geometric fingerprint for Large Reasoning Model (LRM) reasoning capability, emerging rapidly during Supervised Fine-Tuning (SFT) and strengthening further via Reinforcement Learning (RL). Addressing the fundamental gap between token-level behavioral analysis and internal reasoning mechanisms, the authors propose Correlation-Regularized Group Policy Optimization (CorR-PO). This method embeds the inversion signature into RL reward regularization. Extensive experiments on various reasoning benchmarks across multiple model scales demonstrate that CorR-PO consistently outperforms baselines, confirming a direct correlation between stronger inversion and superior reasoning performance.

Key takeaway

For AI Scientists and Machine Learning Engineers optimizing Large Reasoning Models, understanding Entropy-Gradient Inversion is crucial. This geometric fingerprint directly correlates with reasoning performance and can be integrated into reinforcement learning reward functions. You should consider implementing Correlation-Regularized Group Policy Optimization (CorR-PO) to enhance LRM training stability and achieve superior reasoning capabilities by leveraging this internal mechanism.

Key insights

Entropy-Gradient Inversion, a negative correlation between token entropy and logit gradients, fingerprints LRM reasoning.

Principles

• LRM reasoning capability correlates with Entropy-Gradient Inversion strength.
• Inversion emerges during SFT and strengthens with RL training.

Method

Correlation-Regularized Group Policy Optimization (CorR-PO) embeds Entropy-Gradient Inversion as a reward regularization signal for RL-driven LRM optimization.

In practice

• Analyze token entropy and logit gradients in LRM training.
• Integrate inversion signatures into RL reward functions.

Topics

• Large Reasoning Models
• Entropy-Gradient Inversion
• Reinforcement Learning
• Policy Optimization
• Token Entropy
• Logit Gradients

Best for: NLP Engineer, Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.