🚀 Will Agentic ESOpt Be the Future of Long-Horizon Agentic Fine-Tuning? Excited to share our new work on Arxiv. Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Memory Requirements As LLM agents become longer-horizon and more branching, conventional Agentic RL faces three growing bottlenecks: high training/memory cost and increasingly challenging long-horizon credit assignment. Our Agentic ESOpt approaches agent fine-tuning from the parameter space instead, targeting three advantages: ⚡ Model Scalability — only minimal inference-level GPU memory requirement 🔧 Flexibility — prompt/skill–parameter co-evolution ⏳ Long-Horizon Scalability — trajectory-level parameter attribution without horizon-wise decomposition There are impressive empirical results, and we believe ES may be a better-matched parameter fine-tuning paradigm as agents become larger and longer-horizon. 📄 Paper: arxiv.org/abs/2608.17310 🌐 Project: zz1358m.github.io/Project-Ag… 💻 GitHub: github.com/zz1358m/Agentic-E… 🤗 Models & Checkpoints: huggingface.co/collections/z… Code, training logs, and checkpoints have been fully open-sourced. Main takeaways ↓