rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
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2501.04519
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Ksenia Se
Kseniase
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11 new types of RAG
RAG is evolving fast, keeping pace with cutting-edge AI trends. Today it becomes more agentic and smarter at navigating complex structures like hypergraphs.
Here are 11 latest RAG types:
1. InstructRAG -> https://huggingface.co/papers/2504.13032
Combines RAG with a multi-agent framework, using a graph-based structure, an RL agent to expand task coverage, and a meta-learning agent for better generalization
2. CoRAG (Collaborative RAG) -> https://huggingface.co/papers/2504.01883
A collaborative framework that extends RAG to settings where clients train a shared model using a joint passage store
3. ReaRAG -> https://huggingface.co/papers/2503.21729
It uses a Thought-Action-Observation loop to decide at each step whether to retrieve information or finalize an answer, reducing unnecessary reasoning and errors
4. MCTS-RAG -> https://huggingface.co/papers/2503.20757
Combines RAG with Monte Carlo Tree Search (MCTS) to help small LMs handle complex, knowledge-heavy tasks
5. Typed-RAG - > https://huggingface.co/papers/2503.15879
Improves answers on open-ended questions by identifying question types (a debate, personal experience, or comparison) and breaking it down into simpler parts
6. MADAM-RAG -> https://huggingface.co/papers/2504.13079
A multi-agent system where models debate answers over multiple rounds and an aggregator filters noise and misinformation
7. HM-RAG -> https://huggingface.co/papers/2504.12330
A hierarchical multi-agent RAG framework that uses 3 agents: one to split queries, one to retrieve across multiple data types (text, graphs and web), and one to merge and refine answers
8. CDF-RAG -> https://huggingface.co/papers/2504.12560
Works with causal graphs and enables multi-hop causal reasoning, refining queries. It validates responses against causal pathways
To explore what is Causal AI, read our article: https://www.turingpost.com/p/causalai
Subscribe to the Turing Post: https://www.turingpost.com/subscribe
Read further π
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4 days ago
11 new types of RAG
RAG is evolving fast, keeping pace with cutting-edge AI trends. Today it becomes more agentic and smarter at navigating complex structures like hypergraphs.
Here are 11 latest RAG types:
1. InstructRAG -> https://huggingface.co/papers/2504.13032
Combines RAG with a multi-agent framework, using a graph-based structure, an RL agent to expand task coverage, and a meta-learning agent for better generalization
2. CoRAG (Collaborative RAG) -> https://huggingface.co/papers/2504.01883
A collaborative framework that extends RAG to settings where clients train a shared model using a joint passage store
3. ReaRAG -> https://huggingface.co/papers/2503.21729
It uses a Thought-Action-Observation loop to decide at each step whether to retrieve information or finalize an answer, reducing unnecessary reasoning and errors
4. MCTS-RAG -> https://huggingface.co/papers/2503.20757
Combines RAG with Monte Carlo Tree Search (MCTS) to help small LMs handle complex, knowledge-heavy tasks
5. Typed-RAG - > https://huggingface.co/papers/2503.15879
Improves answers on open-ended questions by identifying question types (a debate, personal experience, or comparison) and breaking it down into simpler parts
6. MADAM-RAG -> https://huggingface.co/papers/2504.13079
A multi-agent system where models debate answers over multiple rounds and an aggregator filters noise and misinformation
7. HM-RAG -> https://huggingface.co/papers/2504.12330
A hierarchical multi-agent RAG framework that uses 3 agents: one to split queries, one to retrieve across multiple data types (text, graphs and web), and one to merge and refine answers
8. CDF-RAG -> https://huggingface.co/papers/2504.12560
Works with causal graphs and enables multi-hop causal reasoning, refining queries. It validates responses against causal pathways
To explore what is Causal AI, read our article: https://www.turingpost.com/p/causalai
Subscribe to the Turing Post: https://www.turingpost.com/subscribe
Read further π
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