Not All Layers Need Tuning: Selective Layer Restoration Recovers Diversity
#LLM #Mode Collapse #Selective Layer Restoration #Post-training #Neural Networks #Generation Diversity #arXiv
📌 Key Takeaways
- Post-training processes like SFT and RLHF improve instruction-following but cause 'mode collapse.'
- Mode collapse results in repetitive and less diverse AI-generated outputs in open-ended tasks.
- The research suggests that diversity loss is localized to specific layers within a Large Language Model.
- Restoring selected layers to their pre-trained weights can recover output diversity without losing helpfulness.
📖 Full Retelling
Researchers specializing in artificial intelligence published a technical paper on arXiv on February 11, 2025, detailing a new method titled 'Selective Layer Restoration' to solve the problem of 'mode collapse' in Large Language Models (LLMs). The study addresses a common industry issue where post-training—the process used to make models more helpful and better at following instructions—inadvertently reduces the diversity of the AI's creative output. By identifying that specific functional roles are distributed across different layers of a neural network, the team proposes that restoring certain segments of a model to their original pre-trained state can recover lost variety without sacrificing performance.
The phenomenon of mode collapse has long plagued developers, manifesting as repetitive or predictable text when AI models are used in open-ended or creative settings. While techniques like Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) are essential for aligning AI behavior with human expectations, they often act as a double-edged sword by narrowing the model's 'imagination.' The researchers argue that these fine-tuning processes do not need to alter every layer of the model's architecture, as the core knowledge and linguistic variety often reside in layers that are unnecessarily overwrought during the alignment phase.
To mitigate this, the proposed Selective Layer Restoration technique suggests a surgical approach to model optimization. By hypothesis, certain layers are more responsible for the loss of diversity than others. By reverting a carefully selected range of these layers back to their initial, pre-trained weights, the researchers claim they can re-inject original complexity into the model's responses. This method represents a significant shift from uniform tuning, offering a more nuanced way to maintain a balance between a model's ability to follow complex instructions and its capacity to generate unique, non-repetitive content across diverse applications.
🏷️ Themes
Artificial Intelligence, Machine Learning, Technology
📚 Related People & Topics
Neural network
Structure in biology and artificial intelligence
A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or mathematical models. While individual neurons are simple, many of them together in a network can perform complex tasks.
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
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Connections for Neural network:
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- 🌐 Reinforcement learning (2 shared articles)
- 🌐 Machine learning (2 shared articles)
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- 🌐 Global workspace theory (1 shared articles)
- 🌐 Cognitive neuroscience (1 shared articles)
- 🌐 Robustness (1 shared articles)
- 🌐 Homeostasis (1 shared articles)
📄 Original Source Content
arXiv:2602.06665v1 Announce Type: cross Abstract: Post-training improves instruction-following and helpfulness of large language models (LLMs) but often reduces generation diversity, which leads to repetitive outputs in open-ended settings, a phenomenon known as mode collapse. Motivated by evidence that LLM layers play distinct functional roles, we hypothesize that mode collapse can be localized to specific layers and that restoring a carefully chosen range of layers to their pre-trained weight