Скачать или смотреть Solving the "Alignment Tax": How Entropy-Adaptive Fine-Tuning (EAFT) Prevents LLM Forgetting

Welcome to this episode of SciPulse, where we break down the latest breakthroughs in artificial intelligence. In today’s deep-dive podcast, we explore a significant leap in Large Language Model (LLM) training: Entropy-Adaptive Fine-Tuning (EAFT).

Fine-tuning an AI on a specific subject—like medicine or complex mathematics—often comes with a hidden cost known as the "Alignment Tax" or catastrophic forgetting. This is the process in which a model acquires specialized skills but loses its general intelligence and reasoning capabilities.

Researchers from Beijing University of Posts and Telecommunications have discovered that the primary culprit is something they call "Confident Conflicts".

In this episode, we discuss:

• The Root of Forgetting: Why standard Supervised Fine-Tuning (SFT) forces models to "memorize" data that contradicts their existing knowledge, leading to destructive gradient updates.

• The Entropy Solution: How EAFT acts as a "soft gating" mechanism, using token-level entropy to distinguish between valid new information and destructive knowledge conflicts.

• Efficiency & Performance: How this method achieves a Pareto improvement, matching the performance of standard fine-tuning in math, medical, and agentic domains while protecting the model's core intelligence.

• Universal Scalability: Why this technique works across diverse model families, including Qwen and GLM, at scales ranging from 4B to 32B parameters.

Whether you are an AI researcher, a student, or a tech enthusiast, this conversation will change how you think about "teaching" AI without breaking what it already knows.

Educational Disclaimer: This podcast episode is an automated overview and analysis of the research paper "Entropy-Adaptive Fine-Tuning: Resolving Confident Conflicts to Mitigate Forgetting." It is intended for educational purposes and is not a substitute for reading the original peer-reviewed research. Viewers should consult the full paper for technical methodologies and data verification.

Original Research Paper: https://arxiv.org/pdf/2601.02151

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