Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual Tokens
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2506.17218
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19
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10 Techniques for Boosting LLM Reasoning in 2025
Everyone’s chasing top reasoning, but sometimes it's still the bottleneck for many real-world tasks. This week, let's spotlight some powerful techniques that have shown promise in helping LLMs achieve more consistent logic, planning, and depth:
1. Retrieval-Augmented CoT Chaining (RAG+CoT) -> CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models (2504.13534)
Combines Chain-of-Thought prompting with retrieval augmentation at intermediate steps. Relevant documents are fetched after each reasoning subgoal, updating context dynamically. Great for open-domain QA, math, logic and multi-hop fact-checking
2. Tool-use by example injection -> Self-Training Large Language Models for Tool-Use Without Demonstrations (2502.05867)
Injects few-shot tool interaction examples during training to implicitly teach calling patterns. Helps in plug-and-play tool use without training new architectures
3. Visual Scratchpads, or multimodal reasoning support -> Imagine while Reasoning in Space: Multimodal Visualization-of-Thought (2501.07542)
Using structured visual inputs or sketchable intermediate steps (diagrams, grids, trees) boosts performance in tasks like planning, geometry, and multi-agent simulation. In real practice thanks to this GPT-4o, Claude, and Gemini show marked improvement
4. System 1 vs System 2 Prompt switching -> Adaptive Deep Reasoning: Triggering Deep Thinking When Needed (2505.20101)
Changing a fast, intuitive response prompt with a slow, deliberate reasoning mode is among the most popular AI trends. E.g., models tend to respond more reliably when explicitly instructed to “think like a researcher.” This can also reduce hallucinations in open-ended generation and debate tasks
5. Adversarial Self-Chat Fine-Tuning -> Self-playing Adversarial Language Game Enhances LLM Reasoning (2404.10642)
Generate debates between model variants or model vs human, then fine-tune on the winner’s response. It helps models learn to better defend their reasoning. Used in Claude’s Constitutional AI and SPPO-style tuning
Read further below👇
Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe
Everyone’s chasing top reasoning, but sometimes it's still the bottleneck for many real-world tasks. This week, let's spotlight some powerful techniques that have shown promise in helping LLMs achieve more consistent logic, planning, and depth:
1. Retrieval-Augmented CoT Chaining (RAG+CoT) -> CoT-RAG: Integrating Chain of Thought and Retrieval-Augmented Generation to Enhance Reasoning in Large Language Models (2504.13534)
Combines Chain-of-Thought prompting with retrieval augmentation at intermediate steps. Relevant documents are fetched after each reasoning subgoal, updating context dynamically. Great for open-domain QA, math, logic and multi-hop fact-checking
2. Tool-use by example injection -> Self-Training Large Language Models for Tool-Use Without Demonstrations (2502.05867)
Injects few-shot tool interaction examples during training to implicitly teach calling patterns. Helps in plug-and-play tool use without training new architectures
3. Visual Scratchpads, or multimodal reasoning support -> Imagine while Reasoning in Space: Multimodal Visualization-of-Thought (2501.07542)
Using structured visual inputs or sketchable intermediate steps (diagrams, grids, trees) boosts performance in tasks like planning, geometry, and multi-agent simulation. In real practice thanks to this GPT-4o, Claude, and Gemini show marked improvement
4. System 1 vs System 2 Prompt switching -> Adaptive Deep Reasoning: Triggering Deep Thinking When Needed (2505.20101)
Changing a fast, intuitive response prompt with a slow, deliberate reasoning mode is among the most popular AI trends. E.g., models tend to respond more reliably when explicitly instructed to “think like a researcher.” This can also reduce hallucinations in open-ended generation and debate tasks
5. Adversarial Self-Chat Fine-Tuning -> Self-playing Adversarial Language Game Enhances LLM Reasoning (2404.10642)
Generate debates between model variants or model vs human, then fine-tune on the winner’s response. It helps models learn to better defend their reasoning. Used in Claude’s Constitutional AI and SPPO-style tuning
Read further below👇
Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe
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