Weak-Driven Learning: How Weak Agents make Strong Agents Stronger
#Large Language Models #Post-training #arXiv #WMSS framework #Weak agents #Supervised learning #AI research
📌 Key Takeaways
- Researchers have identified a 'saturation bottleneck' where large language models stop improving after reaching high confidence levels.
- The new WMSS framework utilizes a model's previous 'weak' states to provide informative supervision for its current 'strong' state.
- Traditional post-training methods often suffer from diminishing returns by only reinforcing target predictions.
- The study demonstrates that earlier versions of an AI hold latent signals that can stabilize and enhance advanced model performance.
📖 Full Retelling
🏷️ Themes
Artificial Intelligence, Machine Learning, Model Optimization
📚 Related People & Topics
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...
Artificial intelligence
Intelligence of machines
# Artificial Intelligence (AI) **Artificial Intelligence (AI)** is a specialized field of computer science dedicated to the development and study of computational systems capable of performing tasks typically associated with human intelligence. These tasks include learning, reasoning, problem-solvi...
Supervised learning
Machine learning paradigm
In machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based on example input-output pairs. This process involves training a statistical model using labeled data, meaning each piece of input data is provid...
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📄 Original Source Content
arXiv:2602.08222v1 Announce Type: new Abstract: As post-training optimization becomes central to improving large language models, we observe a persistent saturation bottleneck: once models grow highly confident, further training yields diminishing returns. While existing methods continue to reinforce target predictions, we find that informative supervision signals remain latent in models' own historical weak states. Motivated by this observation, we propose WMSS (Weak Agents Can Make Strong Age