Who Deserves the Reward? SHARP: Shapley Credit-based Optimization for Multi-Agent System
#SHARP optimization #Large Language Models #Shapley value #Credit assignment #Multi-agent systems #arXiv #AI training
📌 Key Takeaways
- Researchers have introduced SHARP, a new optimization framework for multi-agent LLM systems based on Shapley values.
- The framework addresses the 'credit assignment' problem, which makes it difficult to pinpoint which AI agent is responsible for task success.
- Unlike traditional methods that use global reward signals, SHARP identifies individual marginal contributions to task completion.
- The innovation aims to make the integration of LLMs with external tools more efficient and easier to train for complex problem-solving.
📖 Full Retelling
🏷️ Themes
Artificial Intelligence, Machine Learning, Multi-Agent Systems
📚 Related People & Topics
Machine learning
Study of algorithms that improve automatically through experience
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, advances i...
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...
Shapley value
Concept in game theory
In cooperative game theory, the Shapley value is a method (solution concept) for fairly distributing the total gains or costs among a group of players who have collaborated. For example, in a team project where each member contributed differently, the Shapley value provides a way to determine how mu...
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Connections for Machine learning:
- 🌐 Large language model (7 shared articles)
- 🌐 Generative artificial intelligence (3 shared articles)
- 🌐 Electroencephalography (3 shared articles)
- 🌐 Natural language processing (2 shared articles)
- 🌐 Artificial intelligence (2 shared articles)
- 🌐 Graph neural network (2 shared articles)
- 🌐 Neural network (2 shared articles)
- 🌐 Computer vision (2 shared articles)
- 🌐 Transformer (1 shared articles)
- 🌐 User interface (1 shared articles)
- 👤 Stuart Russell (1 shared articles)
- 🌐 Ethics of artificial intelligence (1 shared articles)
📄 Original Source Content
arXiv:2602.08335v1 Announce Type: new Abstract: Integrating Large Language Models (LLMs) with external tools via multi-agent systems offers a promising new paradigm for decomposing and solving complex problems. However, training these systems remains notoriously difficult due to the credit assignment challenge, as it is often unclear which specific functional agent is responsible for the success or failure of decision trajectories. Existing methods typically rely on sparse or globally broadcast