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End-to-End Goal-Driven Web Navigation
Paper • 1602.02261 • Published -
Learning Language Games through Interaction
Paper • 1606.02447 • Published -
Naturalizing a Programming Language via Interactive Learning
Paper • 1704.06956 • Published -
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
Paper • 1802.08802 • Published • 1
Collections
Discover the best community collections!
Collections including paper arxiv:2305.16291
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Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models
Paper • 2310.04406 • Published • 10 -
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Paper • 2305.10601 • Published • 13 -
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
Paper • 2404.02575 • Published • 51 -
Voyager: An Open-Ended Embodied Agent with Large Language Models
Paper • 2305.16291 • Published • 11
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Training Software Engineering Agents and Verifiers with SWE-Gym
Paper • 2412.21139 • Published • 24 -
Evaluating Language Models as Synthetic Data Generators
Paper • 2412.03679 • Published • 49 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 152 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 118
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Re3: Generating Longer Stories With Recursive Reprompting and Revision
Paper • 2210.06774 • Published • 2 -
Constitutional AI: Harmlessness from AI Feedback
Paper • 2212.08073 • Published • 3 -
AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls
Paper • 2402.04253 • Published -
Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
Paper • 2305.19118 • Published
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Stabilizing RLHF through Advantage Model and Selective Rehearsal
Paper • 2309.10202 • Published • 11 -
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
Paper • 2309.10150 • Published • 25 -
Robotic Offline RL from Internet Videos via Value-Function Pre-Training
Paper • 2309.13041 • Published • 8 -
Voyager: An Open-Ended Embodied Agent with Large Language Models
Paper • 2305.16291 • Published • 11
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Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
Paper • 2211.04325 • Published • 1 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 21 -
On the Opportunities and Risks of Foundation Models
Paper • 2108.07258 • Published • 1 -
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Paper • 2204.07705 • Published • 2
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TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks
Paper • 2412.14161 • Published • 52 -
Training Software Engineering Agents and Verifiers with SWE-Gym
Paper • 2412.21139 • Published • 24 -
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis
Paper • 2412.19723 • Published • 88 -
AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation
Paper • 2408.00764 • Published • 1
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Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
Paper • 2312.14878 • Published • 15 -
War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars
Paper • 2311.17227 • Published -
Voyager: An Open-Ended Embodied Agent with Large Language Models
Paper • 2305.16291 • Published • 11 -
Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs
Paper • 2311.05657 • Published • 32
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More Agents Is All You Need
Paper • 2402.05120 • Published • 58 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 118 -
K-Level Reasoning with Large Language Models
Paper • 2402.01521 • Published • 18 -
Voyager: An Open-Ended Embodied Agent with Large Language Models
Paper • 2305.16291 • Published • 11
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Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents
Paper • 2302.01560 • Published • 1 -
Voyager: An Open-Ended Embodied Agent with Large Language Models
Paper • 2305.16291 • Published • 11 -
LASER: LLM Agent with State-Space Exploration for Web Navigation
Paper • 2309.08172 • Published • 13 -
A Data Source for Reasoning Embodied Agents
Paper • 2309.07974 • Published • 7
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End-to-End Goal-Driven Web Navigation
Paper • 1602.02261 • Published -
Learning Language Games through Interaction
Paper • 1606.02447 • Published -
Naturalizing a Programming Language via Interactive Learning
Paper • 1704.06956 • Published -
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
Paper • 1802.08802 • Published • 1
-
Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
Paper • 2211.04325 • Published • 1 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 21 -
On the Opportunities and Risks of Foundation Models
Paper • 2108.07258 • Published • 1 -
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Paper • 2204.07705 • Published • 2
-
Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models
Paper • 2310.04406 • Published • 10 -
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Paper • 2305.10601 • Published • 13 -
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
Paper • 2404.02575 • Published • 51 -
Voyager: An Open-Ended Embodied Agent with Large Language Models
Paper • 2305.16291 • Published • 11
-
TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks
Paper • 2412.14161 • Published • 52 -
Training Software Engineering Agents and Verifiers with SWE-Gym
Paper • 2412.21139 • Published • 24 -
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis
Paper • 2412.19723 • Published • 88 -
AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation
Paper • 2408.00764 • Published • 1
-
Training Software Engineering Agents and Verifiers with SWE-Gym
Paper • 2412.21139 • Published • 24 -
Evaluating Language Models as Synthetic Data Generators
Paper • 2412.03679 • Published • 49 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 152 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 118
-
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
Paper • 2312.14878 • Published • 15 -
War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars
Paper • 2311.17227 • Published -
Voyager: An Open-Ended Embodied Agent with Large Language Models
Paper • 2305.16291 • Published • 11 -
Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs
Paper • 2311.05657 • Published • 32
-
Re3: Generating Longer Stories With Recursive Reprompting and Revision
Paper • 2210.06774 • Published • 2 -
Constitutional AI: Harmlessness from AI Feedback
Paper • 2212.08073 • Published • 3 -
AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls
Paper • 2402.04253 • Published -
Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
Paper • 2305.19118 • Published
-
More Agents Is All You Need
Paper • 2402.05120 • Published • 58 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 118 -
K-Level Reasoning with Large Language Models
Paper • 2402.01521 • Published • 18 -
Voyager: An Open-Ended Embodied Agent with Large Language Models
Paper • 2305.16291 • Published • 11
-
Stabilizing RLHF through Advantage Model and Selective Rehearsal
Paper • 2309.10202 • Published • 11 -
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
Paper • 2309.10150 • Published • 25 -
Robotic Offline RL from Internet Videos via Value-Function Pre-Training
Paper • 2309.13041 • Published • 8 -
Voyager: An Open-Ended Embodied Agent with Large Language Models
Paper • 2305.16291 • Published • 11
-
Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents
Paper • 2302.01560 • Published • 1 -
Voyager: An Open-Ended Embodied Agent with Large Language Models
Paper • 2305.16291 • Published • 11 -
LASER: LLM Agent with State-Space Exploration for Web Navigation
Paper • 2309.08172 • Published • 13 -
A Data Source for Reasoning Embodied Agents
Paper • 2309.07974 • Published • 7