#computational-optimization2 articles

2 articles

AIBullisharXiv – CS AI · 4h ago3

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RUMAD: Reinforcement-Unifying Multi-Agent Debate

Researchers introduce RUMAD, a reinforcement learning framework that optimizes multi-agent AI debate systems by dynamically controlling communication topology. The system achieves over 80% reduction in computational costs while improving reasoning accuracy across benchmark tests, with strong generalization capabilities across different task domains.

AIBullisharXiv – CS AI · 4h ago4

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FineScope : SAE-guided Data Selection Enables Domain Specific LLM Pruning and Finetuning

Researchers introduce FineScope, a framework that uses Sparse Autoencoder (SAE) techniques to create smaller, domain-specific language models from larger pretrained LLMs through structured pruning and self-data distillation. The method achieves competitive performance while significantly reducing computational requirements compared to training from scratch.