DiscoverArxiv PapersTransformers Struggle to Learn to Search
Transformers Struggle to Learn to Search

Transformers Struggle to Learn to Search

Update: 2024-12-09
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This study investigates transformers' search capabilities using graph connectivity, revealing that while they can learn to search, performance declines with larger graphs, unaffected by model size or in-context learning.




https://arxiv.org/abs//2412.04703




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In Channel
[QA] Transformers Struggle to Learn to Search

[QA] Transformers Struggle to Learn to Search

2024-12-0907:41

Transformers Struggle to Learn to Search

Transformers Struggle to Learn to Search

2024-12-0919:58

[QA] Navigation World Models

[QA] Navigation World Models

2024-12-0707:36

Navigation World Models

Navigation World Models

2024-12-0719:47

[QA] Motion Prompting: Controlling Video Generation with Motion Trajectories

[QA] Motion Prompting: Controlling Video Generation with Motion Trajectories

2024-12-0707:21

Motion Prompting: Controlling Video Generation with Motion Trajectories

Motion Prompting: Controlling Video Generation with Motion Trajectories

2024-12-0716:36

[QA] Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis

[QA] Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis

2024-12-0608:01

Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis

Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis

2024-12-0621:32

[QA] NVILA: Efficient Frontier Visual Language Models

[QA] NVILA: Efficient Frontier Visual Language Models

2024-12-0607:38

NVILA: Efficient Frontier Visual Language Models

NVILA: Efficient Frontier Visual Language Models

2024-12-0623:11

[QA] o1-Coder: an o1 Replication for Coding

[QA] o1-Coder: an o1 Replication for Coding

2024-12-0309:05

o1-Coder: an o1 Replication for Coding

o1-Coder: an o1 Replication for Coding

2024-12-0319:21

[QA] Efficient Track Anything

[QA] Efficient Track Anything

2024-12-0307:55

Efficient Track Anything

Efficient Track Anything

2024-12-0319:04

[QA] Reverse Thinking Makes LLMs Stronger Reasoners

[QA] Reverse Thinking Makes LLMs Stronger Reasoners

2024-12-0208:08

Reverse Thinking Makes LLMs Stronger Reasoners

Reverse Thinking Makes LLMs Stronger Reasoners

2024-12-0216:36

[QA] Critical Tokens Matter: Token-Level Contrastive Estimation Enhence LLM’s Reasoning Capability

[QA] Critical Tokens Matter: Token-Level Contrastive Estimation Enhence LLM’s Reasoning Capability

2024-12-0207:29

Critical Tokens Matter: Token-Level Contrastive Estimation Enhence LLM’s Reasoning Capability

Critical Tokens Matter: Token-Level Contrastive Estimation Enhence LLM’s Reasoning Capability

2024-12-0212:00

[QA] JetFormer: an autoregressive generative model of raw images and text

[QA] JetFormer: an autoregressive generative model of raw images and text

2024-12-0207:29

JetFormer: an autoregressive generative model of raw images and text

JetFormer: an autoregressive generative model of raw images and text

2024-12-0223:02

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Transformers Struggle to Learn to Search

Transformers Struggle to Learn to Search

Igor Melnyk