Difference between revisions of "Increasing AI Intelligence"
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=Inference Time Compute= | =Inference Time Compute= | ||
| − | ==Methods== | + | ===Methods=== |
* 2024-03: [https://arxiv.org/abs/2403.09629 Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking] | * 2024-03: [https://arxiv.org/abs/2403.09629 Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking] | ||
* 2024-11: [https://arxiv.org/pdf/2411.19865 Reverse Thinking Makes LLMs Stronger Reasoners] | * 2024-11: [https://arxiv.org/pdf/2411.19865 Reverse Thinking Makes LLMs Stronger Reasoners] | ||
| Line 67: | Line 67: | ||
* 2024-06: [https://arxiv.org/abs/2406.16838 From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models] | * 2024-06: [https://arxiv.org/abs/2406.16838 From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models] | ||
| − | ==In context learning (ICL), search, and other inference-time methods== | + | ===In context learning (ICL), search, and other inference-time methods=== |
* 2023-03: [https://arxiv.org/abs/2303.11366 Reflexion: Language Agents with Verbal Reinforcement Learning] | * 2023-03: [https://arxiv.org/abs/2303.11366 Reflexion: Language Agents with Verbal Reinforcement Learning] | ||
* 2023-05: [https://arxiv.org/abs/2305.16291 VOYAGER: An Open-Ended Embodied Agent with Large Language Models] | * 2023-05: [https://arxiv.org/abs/2305.16291 VOYAGER: An Open-Ended Embodied Agent with Large Language Models] | ||
| Line 74: | Line 74: | ||
* 2024-09: [https://arxiv.org/abs/2409.03733 Planning In Natural Language Improves LLM Search For Code Generation] | * 2024-09: [https://arxiv.org/abs/2409.03733 Planning In Natural Language Improves LLM Search For Code Generation] | ||
| − | ==Inference-time Sampling== | + | ===Inference-time Sampling=== |
* 2024-10: [https://github.com/xjdr-alt/entropix entropix: Entropy Based Sampling and Parallel CoT Decoding] | * 2024-10: [https://github.com/xjdr-alt/entropix entropix: Entropy Based Sampling and Parallel CoT Decoding] | ||
* 2024-10: [https://arxiv.org/abs/2410.16033 TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling] | * 2024-10: [https://arxiv.org/abs/2410.16033 TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling] | ||
| Line 80: | Line 80: | ||
* 2024-12: [https://arxiv.org/abs/2412.06822 Guidance is All You Need: Temperature-Guided Reasoning in Large Language Models] | * 2024-12: [https://arxiv.org/abs/2412.06822 Guidance is All You Need: Temperature-Guided Reasoning in Large Language Models] | ||
| − | ==Inference-time Gradient== | + | ===Inference-time Gradient=== |
* 2024-11: [https://ekinakyurek.github.io/papers/ttt.pdf The Surprising Effectiveness of Test-Time Training for Abstract Reasoning] ([https://github.com/ekinakyurek/marc code]) | * 2024-11: [https://ekinakyurek.github.io/papers/ttt.pdf The Surprising Effectiveness of Test-Time Training for Abstract Reasoning] ([https://github.com/ekinakyurek/marc code]) | ||
| − | ==Self-prompting== | + | ===Self-prompting=== |
* 2023-05: [https://arxiv.org/abs/2305.09993 Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling] | * 2023-05: [https://arxiv.org/abs/2305.09993 Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling] | ||
* 2023-11: [https://arxiv.org/abs/2311.04205 Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves] | * 2023-11: [https://arxiv.org/abs/2311.04205 Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves] | ||
| − | ==Retrieval or Memory== | + | ===Retrieval or Memory=== |
* 2024-12: [https://arxiv.org/abs/2412.13781 Meta-Reflection: A Feedback-Free Reflection Learning Framework] | * 2024-12: [https://arxiv.org/abs/2412.13781 Meta-Reflection: A Feedback-Free Reflection Learning Framework] | ||
| − | ==In-context thought== | + | ===In-context thought=== |
* 2022-01: [https://arxiv.org/abs/2201.11903 Chain-of-Thought Prompting Elicits Reasoning in Large Language Models] (Google Brain) | * 2022-01: [https://arxiv.org/abs/2201.11903 Chain-of-Thought Prompting Elicits Reasoning in Large Language Models] (Google Brain) | ||
* 2023-05: [https://arxiv.org/abs/2305.10601 Tree of Thoughts: Deliberate Problem Solving with Large Language Models] (Google DeepMind) | * 2023-05: [https://arxiv.org/abs/2305.10601 Tree of Thoughts: Deliberate Problem Solving with Large Language Models] (Google DeepMind) | ||
| Line 102: | Line 102: | ||
* 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] | ||
| − | ==Naive multi-LLM (verification, majority voting, best-of-N, etc.)== | + | ===Naive multi-LLM (verification, majority voting, best-of-N, etc.)=== |
* 2023-06: [https://arxiv.org/abs/2306.02561 LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion] ([https://github.com/yuchenlin/LLM-Blender?tab=readme-ov-file code]) | * 2023-06: [https://arxiv.org/abs/2306.02561 LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion] ([https://github.com/yuchenlin/LLM-Blender?tab=readme-ov-file code]) | ||
* 2023-12: [https://aclanthology.org/2023.findings-emnlp.203/ Dynamic Voting for Efficient Reasoning in Large Language Models] | * 2023-12: [https://aclanthology.org/2023.findings-emnlp.203/ Dynamic Voting for Efficient Reasoning in Large Language Models] | ||
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* 2024-12: [https://github.com/irthomasthomas/llm-consortium llm-consortium]: Multiple LLMs collaboratively solve problems through structured dialogue, evaluation and arbitration | * 2024-12: [https://github.com/irthomasthomas/llm-consortium llm-consortium]: Multiple LLMs collaboratively solve problems through structured dialogue, evaluation and arbitration | ||
| − | ==Multi-LLM (multiple comparisons, branching, etc.)== | + | ===Multi-LLM (multiple comparisons, branching, etc.)=== |
* 2024-10: [https://arxiv.org/abs/2410.10630 Thinking LLMs: General Instruction Following with Thought Generation] | * 2024-10: [https://arxiv.org/abs/2410.10630 Thinking LLMs: General Instruction Following with Thought Generation] | ||
* 2024-11: [https://arxiv.org/abs/2411.02830 Mixtures of In-Context Learners]: Multiple "experts", each with a different set of in-context examples; combine outputs at the level of next-token-prediction | * 2024-11: [https://arxiv.org/abs/2411.02830 Mixtures of In-Context Learners]: Multiple "experts", each with a different set of in-context examples; combine outputs at the level of next-token-prediction | ||
* 2024-11: [https://arxiv.org/abs/2411.10440 LLaVA-o1: Let Vision Language Models Reason Step-by-Step] ([https://github.com/PKU-YuanGroup/LLaVA-o1 code]) | * 2024-11: [https://arxiv.org/abs/2411.10440 LLaVA-o1: Let Vision Language Models Reason Step-by-Step] ([https://github.com/PKU-YuanGroup/LLaVA-o1 code]) | ||
| − | ==Iteration (e.g. neural-like layered blocks)== | + | ===Iteration (e.g. neural-like layered blocks)=== |
* 2024-06: [https://arxiv.org/abs/2406.04692 Mixture-of-Agents Enhances Large Language Model Capabilities] | * 2024-06: [https://arxiv.org/abs/2406.04692 Mixture-of-Agents Enhances Large Language Model Capabilities] | ||
| − | ==Iterative reasoning via graphs== | + | ===Iterative reasoning via graphs=== |
* 2023-08: [https://arxiv.org/abs/2308.09687 Graph of Thoughts: Solving Elaborate Problems with Large Language Models] | * 2023-08: [https://arxiv.org/abs/2308.09687 Graph of Thoughts: Solving Elaborate Problems with Large Language Models] | ||
* 2023-10: [https://arxiv.org/abs/2310.04363 Amortizing intractable inference in large language models] ([https://github.com/GFNOrg/gfn-lm-tuning code]) | * 2023-10: [https://arxiv.org/abs/2310.04363 Amortizing intractable inference in large language models] ([https://github.com/GFNOrg/gfn-lm-tuning code]) | ||
* 2024-09: [https://arxiv.org/abs/2409.10038 On the Diagram of Thought]: Iterative reasoning as a directed acyclic graph (DAG) | * 2024-09: [https://arxiv.org/abs/2409.10038 On the Diagram of Thought]: Iterative reasoning as a directed acyclic graph (DAG) | ||
| − | ==Monte Carlo Tree Search (MCTS)== | + | ===Monte Carlo Tree Search (MCTS)=== |
* 2024-05: [https://arxiv.org/abs/2405.03553 AlphaMath Almost Zero: process Supervision without process] | * 2024-05: [https://arxiv.org/abs/2405.03553 AlphaMath Almost Zero: process Supervision without process] | ||
* 2024-06: [https://arxiv.org/abs/2406.03816 ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search] | * 2024-06: [https://arxiv.org/abs/2406.03816 ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search] | ||
| Line 132: | Line 132: | ||
* 2024-12: [https://arxiv.org/abs/2412.18319 Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search] | * 2024-12: [https://arxiv.org/abs/2412.18319 Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search] | ||
| − | ==Other Search== | + | ===Other Search=== |
* 2024-11: [https://arxiv.org/abs/2411.05010 Scattered Forest Search: Smarter Code Space Exploration with LLMs] | * 2024-11: [https://arxiv.org/abs/2411.05010 Scattered Forest Search: Smarter Code Space Exploration with LLMs] | ||
| − | ==Chain-of-Thought Reasoning== | + | ===Chain-of-Thought Reasoning=== |
* 2017-05: [https://arxiv.org/abs/1705.04146 Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems] | * 2017-05: [https://arxiv.org/abs/1705.04146 Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems] | ||
* 2021-11: [https://arxiv.org/abs/2110.14168 Training Verifiers to Solve Math Word Problems] | * 2021-11: [https://arxiv.org/abs/2110.14168 Training Verifiers to Solve Math Word Problems] | ||
* 2024-02: [https://arxiv.org/abs/2402.10200 Chain-of-Thought Reasoning Without Prompting] | * 2024-02: [https://arxiv.org/abs/2402.10200 Chain-of-Thought Reasoning Without Prompting] | ||
| − | ==Scaling== | + | ===Scaling=== |
* 2021-04: [https://arxiv.org/abs/2104.03113 Scaling Scaling Laws with Board Games] | * 2021-04: [https://arxiv.org/abs/2104.03113 Scaling Scaling Laws with Board Games] | ||
* 2024-03: [https://arxiv.org/abs/2403.02419 Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems] | * 2024-03: [https://arxiv.org/abs/2403.02419 Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems] | ||
| Line 150: | Line 150: | ||
* 2024-11: [https://arxiv.org/abs/2411.17501 Inference Scaling FLaws: The Limits of LLM Resampling with Imperfect Verifiers] | * 2024-11: [https://arxiv.org/abs/2411.17501 Inference Scaling FLaws: The Limits of LLM Resampling with Imperfect Verifiers] | ||
| − | ==Theory== | + | ===Theory=== |
* 2024-02: [https://arxiv.org/abs/2402.12875 Chain of Thought Empowers Transformers to Solve Inherently Serial Problems] | * 2024-02: [https://arxiv.org/abs/2402.12875 Chain of Thought Empowers Transformers to Solve Inherently Serial Problems] | ||
| − | ==Expending compute works== | + | ===Expending compute works=== |
* 2024-06-10: Blog post (opinion): [https://yellow-apartment-148.notion.site/AI-Search-The-Bitter-er-Lesson-44c11acd27294f4495c3de778cd09c8d AI Search: The Bitter-er Lesson] | * 2024-06-10: Blog post (opinion): [https://yellow-apartment-148.notion.site/AI-Search-The-Bitter-er-Lesson-44c11acd27294f4495c3de778cd09c8d AI Search: The Bitter-er Lesson] | ||
* 2024-07-17: Blog post (test): [https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt Getting 50% (SoTA) on ARC-AGI with GPT-4o] | * 2024-07-17: Blog post (test): [https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt Getting 50% (SoTA) on ARC-AGI with GPT-4o] | ||
| Line 160: | Line 160: | ||
* 2024-09-16: [https://www.oneusefulthing.org/p/scaling-the-state-of-play-in-ai Scaling: The State of Play in AI] | * 2024-09-16: [https://www.oneusefulthing.org/p/scaling-the-state-of-play-in-ai Scaling: The State of Play in AI] | ||
| − | ==Code for Inference-time Compute== | + | ===Code for Inference-time Compute=== |
* [https://github.com/codelion/optillm 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) | * [https://github.com/codelion/optillm 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) | ||
Revision as of 15:44, 31 December 2024
Contents
- 1 Reviews
- 2 Prompt Engineering
- 3 Fine Tuning
- 4 Proactive Search
- 5 Inference Time Compute
- 5.1 Methods
- 5.2 In context learning (ICL), search, and other inference-time methods
- 5.3 Inference-time Sampling
- 5.4 Inference-time Gradient
- 5.5 Self-prompting
- 5.6 Retrieval or Memory
- 5.7 In-context thought
- 5.8 Naive multi-LLM (verification, majority voting, best-of-N, etc.)
- 5.9 Multi-LLM (multiple comparisons, branching, etc.)
- 5.10 Iteration (e.g. neural-like layered blocks)
- 5.11 Iterative reasoning via graphs
- 5.12 Monte Carlo Tree Search (MCTS)
- 5.13 Other Search
- 5.14 Chain-of-Thought Reasoning
- 5.15 Scaling
- 5.16 Theory
- 5.17 Expending compute works
- 5.18 Code for Inference-time Compute
- 6 Memory
- 7 Tool Use
- 8 Multi-agent Effort (and Emergent Intelligence)
- 9 ML-like Optimization of LLM Setup
Reviews
Prompt Engineering
Fine Tuning
Proactive Search
Compute expended after training, but before inference.
Training Data (Data Refinement, Synthetic Data)
- C.f. image datasets:
- 2024-09: Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale
- 2024-10: Data Cleaning Using Large Language Models
- Updating list of links: Synthetic Data of LLMs, by LLMs, for LLMs
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
- 2024-11: Language Models are Hidden Reasoners: Unlocking Latent Reasoning Capabilities via Self-Rewarding (code)
Automated prompt generation
Distill inference-time-compute into model
- 2023-10: Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning (U. Maryland, Adobe)
- 2023-11: Implicit Chain of Thought Reasoning via Knowledge Distillation (Harvard, Microsoft, Hopkins)
- 2024-02: Grandmaster-Level Chess Without Search (Google DeepMind)
- 2024-07: Fine-Tuning with Divergent Chains of Thought Boosts Reasoning Through Self-Correction in Language Models
- 2024-07: Distilling System 2 into System 1
- 2024-07: BOND: Aligning LLMs with Best-of-N Distillation
- 2024-09: Training Language Models to Self-Correct via Reinforcement Learning (Google DeepMind)
- 2024-10: Thinking LLMs: General Instruction Following with Thought Generation
- 2024-10: Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces
- 2024-12: Training Large Language Models to Reason in a Continuous Latent Space
CoT reasoning model
- 2024-09: OpenAI o1
- 2024-10: O1 Replication Journey: A Strategic Progress Report – Part 1 (code): Attempt by Walnut Plan to reproduce o1-like in-context reasoning
- 2024-11: DeepSeek-R1-Lite-Preview reasoning model
- 2024-11: Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
- 2024-11: O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?
- 2024-12: o1-Coder: an o1 Replication for Coding (code)
- 2024-12: Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
- 2024-12: Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective
Scaling
- 2024-08: Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling (Google DeepMind)
- 2024-11: Scaling Laws for Pre-training Agents and World Models
Inference Time Compute
Methods
- 2024-03: Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
- 2024-11: Reverse Thinking Makes LLMs Stronger Reasoners
- 2024-12: Training Large Language Models to Reason in a Continuous Latent Space (Chain of Continuous Thought, COCONUT)
Review
In context learning (ICL), search, and other inference-time methods
- 2023-03: Reflexion: Language Agents with Verbal Reinforcement Learning
- 2023-05: VOYAGER: An Open-Ended Embodied Agent with Large Language Models
- 2024-04: Many-Shot In-Context Learning
- 2024-08: Automated Design of Agentic Systems
- 2024-09: Planning In Natural Language Improves LLM Search For Code Generation
Inference-time Sampling
- 2024-10: entropix: Entropy Based Sampling and Parallel CoT Decoding
- 2024-10: TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling
- 2024-11: Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs
- 2024-12: Guidance is All You Need: Temperature-Guided Reasoning in Large Language Models
Inference-time Gradient
Self-prompting
- 2023-05: Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling
- 2023-11: Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves
Retrieval or Memory
In-context thought
- 2022-01: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Google Brain)
- 2023-05: Tree of Thoughts: Deliberate Problem Solving with Large Language Models (Google DeepMind)
- 2024-05: Faithful Logical Reasoning via Symbolic Chain-of-Thought
- 2024-06: A Tree-of-Thoughts to Broaden Multi-step Reasoning across Languages
- 2024-09: To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
- 2024-09: Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning (Agnostiq, Toronto)
- 2024-09: Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models
- 2024-10: A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration (failed reasoning traces can improve CoT)
- 2024-10: Tree of Problems: Improving structured problem solving with compositionality
- 2023-01/2024-10: A Survey on In-context Learning
Naive multi-LLM (verification, majority voting, best-of-N, etc.)
- 2023-06: LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion (code)
- 2023-12: Dynamic Voting for Efficient Reasoning in Large Language Models
- 2024-04: Regularized Best-of-N Sampling to Mitigate Reward Hacking for Language Model Alignment
- 2024-08: Dynamic Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling
- 2024-11: Multi-expert Prompting Improves Reliability, Safety, and Usefulness of Large Language Models
- 2024-12: llm-consortium: Multiple LLMs collaboratively solve problems through structured dialogue, evaluation and arbitration
Multi-LLM (multiple comparisons, branching, etc.)
- 2024-10: Thinking LLMs: General Instruction Following with Thought Generation
- 2024-11: Mixtures of In-Context Learners: Multiple "experts", each with a different set of in-context examples; combine outputs at the level of next-token-prediction
- 2024-11: LLaVA-o1: Let Vision Language Models Reason Step-by-Step (code)
Iteration (e.g. neural-like layered blocks)
Iterative reasoning via graphs
- 2023-08: Graph of Thoughts: Solving Elaborate Problems with Large Language Models
- 2023-10: Amortizing intractable inference in large language models (code)
- 2024-09: On the Diagram of Thought: Iterative reasoning as a directed acyclic graph (DAG)
Monte Carlo Tree Search (MCTS)
- 2024-05: AlphaMath Almost Zero: process Supervision without process
- 2024-06: ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search
- 2024-06: Improve Mathematical Reasoning in Language Models by Automated Process Supervision
- 2024-06: Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B
- 2024-07: Tree Search for Language Model Agents
- 2024-10: Interpretable Contrastive Monte Carlo Tree Search Reasoning
- 2024-12: Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
Other Search
Chain-of-Thought Reasoning
- 2017-05: Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems
- 2021-11: Training Verifiers to Solve Math Word Problems
- 2024-02: Chain-of-Thought Reasoning Without Prompting
Scaling
- 2021-04: Scaling Scaling Laws with Board Games
- 2024-03: Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems
- 2024-04: The Larger the Better? Improved LLM Code-Generation via Budget Reallocation
- 2024-07: Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
- 2024-08: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models
- 2024-08: Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
- 2024-10: (comparing fine-tuning to in-context learning) Is In-Context Learning Sufficient for Instruction Following in LLMs?
- 2024-11: Inference Scaling FLaws: The Limits of LLM Resampling with Imperfect Verifiers
Theory
Expending compute works
- 2024-06-10: Blog post (opinion): AI Search: The Bitter-er Lesson
- 2024-07-17: Blog post (test): Getting 50% (SoTA) on ARC-AGI with GPT-4o
- 2024-09-12: OpenAI o1: Learning to Reason with LLMs
- 2024-09-16: Scaling: The State of Play in AI
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)
Memory
Tool Use
- 2024-11: DynaSaur: Large Language Agents Beyond Predefined Actions: writes functions/code to increase capabilities
Multi-agent Effort (and Emergent Intelligence)
- 2024-10: Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence
- 2024-10: Agent-as-a-Judge: Evaluate Agents with Agents
- 2024-10: Two are better than one: Context window extension with multi-grained self-injection
- 2024-11: Project Sid: Many-agent simulations toward AI civilization
ML-like Optimization of LLM Setup
- 2023-03: DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines (code: Programming—not prompting—Foundation Models)
- 2024-05: Automatic Prompt Optimization with "Gradient Descent" and Beam Search
- 2024-06: TextGrad: Automatic "Differentiation" via Text (gradient backpropagation through text)
- 2024-06: Symbolic Learning Enables Self-Evolving Agents (optimize LLM frameworks)
