Difference between revisions of "Increasing AI Intelligence"

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(→‎Usage of Reasoning Compute)
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* 2023-01/2024-10: [https://arxiv.org/abs/2301.00234 A Survey on In-context Learning]
 
* 2023-01/2024-10: [https://arxiv.org/abs/2301.00234 A Survey on In-context Learning]
 
* 2025-01: [https://arxiv.org/abs/2501.04682 Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought]
 
* 2025-01: [https://arxiv.org/abs/2501.04682 Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought]
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* [https://x.com/dav1d_bai/status/1904057766593138841 2025-03]: [https://optimal-test-time.vercel.app/papers/accuracy-efficiency-tradeoffs Interruption is All You Need: Improving Reasoning Model Refusal Rates through measuring Parallel Reasoning Diversity]: A novel approach to reducing hallucinations in large language models through parallel reasoning and diversity measurement
  
 
===Naive multi-LLM (verification, majority voting, best-of-N, etc.)===
 
===Naive multi-LLM (verification, majority voting, best-of-N, etc.)===

Revision as of 11:30, 24 March 2025

Reviews

Prompt Engineering

Thought Templates

Automatic Prompt Optimization

Fine Tuning

Proactive Search

Compute expended after training, but before inference.

Training Data (Data Refinement, Synthetic Data)

Re-captioning

Generate consistent plans/thoughts

  • 2024-08: Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers (code)
    • (Microsoft) rStar is a self-play mutual reasoning approach. A small model adds to MCTS using some defined reasoning heuristics. Mutually consistent trajectories can be emphasized.
  • 2024-09: Self-Harmonized Chain of Thought
    • Produce refined chain-of-thought style solutions/prompts for diverse problems. Given a large set of problems/questions, first aggregated semantically, then apply zero-shot chain-of-thought to each problem. Then cross-pollinate between proposed solutions to similar problems, looking for refined and generalize solutions.
  • 2024-11: LLMs Do Not Think Step-by-step In Implicit Reasoning
    • They argue that models trained to reproduce CoT outputs do not, internally, perform stepwise reasoning (with intermediate representations); this suggests that explicit CoT could be superior to implicit CoT.

Sampling

Automated prompt generation

Distill inference-time-compute into model

CoT reasoning model

See also: AI tools > LLM > Open-weights LLM > Reasoning

Scaling

Inference Time Compute

Methods

Review

In context learning (ICL), search, and other inference-time methods

Inference-time Sampling

Inference-time Gradient

Self-prompting

Retrieval or Memory

In-context thought

Naive multi-LLM (verification, majority voting, best-of-N, etc.)

Multi-LLM (multiple comparisons, branching, etc.)

Iteration (e.g. neural-like layered blocks)

Iterative reasoning via graphs

Monte Carlo Tree Search (MCTS)

Other Search

Chain-of-Thought Reasoning

Meta-methods

Analysis

Scaling

Usage of Reasoning Compute

Usage of Training Data

  • 2025-02: LIMO: Less is More for Reasoning (surprisingly easy generalization, from very few reasoning training examples; model can go from knowledge-retrieval to diverse reasoning using curated examples)

Theory

Expending compute works

Compute.png

Pragmatics

Code for Inference-time Compute

  • optillm: Inference proxy which implements state-of-the-art techniques to improve accuracy and performance of LLMs (improve reasoning over coding, logical and mathematical queries)

Interact with Environment

Memory

Tool Use

Integrated

Multi-agent Effort (and Emergent Intelligence)

ML-like Optimization of LLM Setup

Limitations/Requirements

Creativity

See Also

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