Large Language Models (LLMs) equipped with modern Retrieval-Augmented Generation (RAG) systems often employ multi-turn interaction pipelines to interface with search engines for complex reasoning tasks. However, such multi-turn interactions inevitably produce long intermediate contexts, as context length grows exponentially with exploration depth. This leads to a well-known limitation of LLMs: their difficulty in effectively leveraging information from long contexts. This problem is further amplified in RAG systems that depend on in-context learning, where few-shot demonstrations must also be included in the prompt, compounding the context-length bottleneck. To address these challenges, we propose Mujica-MyGo, a unified framework for efficient multi-turn reasoning in RAG. Inspired by the divide-and-conquer principle, we introduce Mujica (Multi-hop Joint Intelligence for Complex Question Answering), a multi-agent RAG workflow that decomposes multi-turn interactions into cooperative sub-interactions, thereby mitigating long-context issues. To eliminate the dependency on in-context learning, we further develop MyGO (Minimalist Policy Gradient Optimization), a lightweight and efficient reinforcement learning algorithm that enables effective post-training of LLMs within complex RAG pipelines. We provide theoretical guarantees for MyGO's convergence to the optimal policy. Empirical evaluations across diverse question-answering benchmarks, covering both text corpora and knowledge graphs, show that Mujica-MyGO achieves superior performance.

His concern is warranted and will require us to strike a balance between protecting the democratic and egalitarian values that made the Internet great to begin with while ensuring those values are used for good. The fundamental issue, then, in creating a 21st-century Internet becomes what changes are warranted and who will be responsible for defining and administering them. On the technology dimension, computer scientists and engineers must develop smarter systems for detecting, addressing, and preventing malicious content on the Web. Cerf's argument on behalf of user training is helpful but will not ultimately solve the problem of an untrustworthy, ungovernable, potentially malicious network. I myself recently fell for a phishing attack, which only proves that today's attacks can fool even savvy, experienced users.