How to Improve Your Agents: Academic Lit Review
Feb 22, 2025 · 39:02
Joe from Columbia University and founder of Arklex AI explains research on AI agents, focusing on improving their reasoning and planning through self-reflection, test-time compute, and tree search methods, without relying on human supervision. He details the TriPath method, which uses larger models to edit smaller models' feedback for better self-improvement, achieving up to 48% accuracy on math benchmarks. He shows how multi-color tree search (MCTS) with contrastive reflection and multi-agent debate (RMCTS) outperforms other search methods on Visual Web Arena and OS World, achieving top non-trained results. He proposes exploratory learning, where models learn from search trajectories rather than optimal actions, improving performance under compute budgets. Finally, he presents the Arklex open-source agent framework, combining machine learning, systems, and security for practical multi-agent orchestration.
