Latent Agents: A Post-Training Procedure for Internalized Multi-Agent Debate
📰 ArXiv cs.AI
Learn to improve LLM reasoning with latent agents, a post-training procedure for internalized multi-agent debate, to enhance efficiency and performance
Action Steps
- Implement a two-stage fine-tuning pipeline to combine debate structure learning with internalization
- Use dynamic reward scheduling to optimize the debate process
- Apply length clipping to reduce computational costs
- Evaluate the performance of the latent agents framework using benchmark datasets
- Fine-tune the model using the proposed framework to improve LLM reasoning
Who Needs to Know This
NLP engineers and AI researchers can benefit from this technique to optimize their language models, improving their overall performance and reducing computational costs
Key Insight
💡 Latent agents can internalize multi-agent debate, reducing computational costs and improving LLM performance
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🤖 Improve LLM reasoning with latent agents! 📚
Key Takeaways
Learn to improve LLM reasoning with latent agents, a post-training procedure for internalized multi-agent debate, to enhance efficiency and performance
Full Article
Title: Latent Agents: A Post-Training Procedure for Internalized Multi-Agent Debate
Abstract:
arXiv:2604.24881v1 Announce Type: new Abstract: Multi-agent debate has been shown to improve reasoning in large language models (LLMs). However, it is compute-intensive, requiring generation of long transcripts before answering questions. To address this inefficiency, we develop a framework that distills multi-agent debate into a single LLM through a two-stage fine-tuning pipeline combining debate structure learning with internalization via dynamic reward scheduling and length clipping. Across m
Abstract:
arXiv:2604.24881v1 Announce Type: new Abstract: Multi-agent debate has been shown to improve reasoning in large language models (LLMs). However, it is compute-intensive, requiring generation of long transcripts before answering questions. To address this inefficiency, we develop a framework that distills multi-agent debate into a single LLM through a two-stage fine-tuning pipeline combining debate structure learning with internalization via dynamic reward scheduling and length clipping. Across m
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