RAG Is Over: RL Agents Are the New Retrieval Stack
3 days ago
- #Retrieval-Augmented Generation
- #Agentic Search
- #Reinforcement Learning
- RAG (Retrieval-Augmented Generation) has reached its performance ceiling, while RL-trained agents have surpassed it.
- Traditional search pipelines involved sparse search (BM25/SPLADE), dense embedding search, and reranking with RRF or cross-encoders.
- Agentic search, where LLMs use tools in a loop, outperforms single-step search pipelines but is expensive and slow.
- Multi-hop retrieval (e.g., Baleen) improved search quality but wasn't transformative due to cost and latency.
- Modern agents can use multiple tools (grep, embedding search, structured data) and solve complex search tasks proficiently.
- RL (Reinforcement Learning) enhances agentic search, making it more viable and efficient compared to non-RL approaches.
- Recent research (DeepRetrieval, Search-R1) shows RL-trained models outperform RAG baselines by significant margins (21-26%).
- RL-trained models excel at retrieval, knowing which tools to use and synthesizing information into coherent answers.
- Specialized small models for retrieval and frontier models for generation may become the standard to optimize efficiency.
- Grok Code (xAI) demonstrates the power of RL in agentic search, being fast and efficient in coding workflows.
- Startups like Happenstance and Clado are adopting RL-powered agentic search, but optimization is needed for speed.
- RL-powered agentic search is becoming the meta, offering superior performance and efficiency over traditional methods.
- Inference.net offers custom model training for RL-powered agentic search, data extraction, and real-time chat.