Shichengf/entropy-acc-traces
Preview • Updated • 11
shicheng
Shichengf
AI & ML interests
Causality, AI for Science(Modular Dynamics), Reinforcement Learning, LLM
Recent Activity
published a dataset 13 days ago
Shichengf/entropy-acc-traces updated a dataset 19 days ago
Shichengf/entropy-acc-traces posted an update about 1 month ago
We are happy to share our new survey:
Scaling LLM Agent Learning with Data Synthesis: A Comprehensive Survey
LLM agents are moving from passive chatbots to interactive systems that use memory, tools, planning, and external environments. Scaling agent learning requires more than input-output pairs: agents need synthetic tasks, trajectories, feedback signals, and environments that can support long-horizon interaction.
In this survey, we organize data synthesis for LLM agent learning around four core artifacts:
Task-level synthesis
Trajectory-level synthesis
Feedback-level synthesis
Environment-level synthesis
We also review quality control, learning frameworks, synthetic evaluation data, and applications across software engineering, agentic search, AI for science, social good, and AI safety/security.
Links:
ResearchGate: [https://www.researchgate.net/publication/406488336_Scaling_LLM_Agent_Learning_with_Data_Synthesis_A_Comprehensive_Survey](https://www.researchgate.net/publication/406488336_Scaling_LLM_Agent_Learning_with_Data_Synthesis_A_Comprehensive_Survey)
OpenReview: [https://openreview.net/forum?id=pQYwkpYmLy](https://openreview.net/forum?id=pQYwkpYmLy)
arXiv: under moderation, link coming soon
#LLMAgents #DataSynthesis #AgentLearning #LLM #Survey
Organizations
published a
dataset 13 days ago
updated a
dataset 19 days ago
posted an update about 1 month ago
Post
102
We are happy to share our new survey:
Scaling LLM Agent Learning with Data Synthesis: A Comprehensive Survey
LLM agents are moving from passive chatbots to interactive systems that use memory, tools, planning, and external environments. Scaling agent learning requires more than input-output pairs: agents need synthetic tasks, trajectories, feedback signals, and environments that can support long-horizon interaction.
In this survey, we organize data synthesis for LLM agent learning around four core artifacts:
Task-level synthesis
Trajectory-level synthesis
Feedback-level synthesis
Environment-level synthesis
We also review quality control, learning frameworks, synthetic evaluation data, and applications across software engineering, agentic search, AI for science, social good, and AI safety/security.
Links:
ResearchGate: [https://www.researchgate.net/publication/406488336_Scaling_LLM_Agent_Learning_with_Data_Synthesis_A_Comprehensive_Survey](https://www.researchgate.net/publication/406488336_Scaling_LLM_Agent_Learning_with_Data_Synthesis_A_Comprehensive_Survey)
OpenReview: [https://openreview.net/forum?id=pQYwkpYmLy](https://openreview.net/forum?id=pQYwkpYmLy)
arXiv: under moderation, link coming soon
#LLMAgents #DataSynthesis #AgentLearning #LLM #Survey
Scaling LLM Agent Learning with Data Synthesis: A Comprehensive Survey
LLM agents are moving from passive chatbots to interactive systems that use memory, tools, planning, and external environments. Scaling agent learning requires more than input-output pairs: agents need synthetic tasks, trajectories, feedback signals, and environments that can support long-horizon interaction.
In this survey, we organize data synthesis for LLM agent learning around four core artifacts:
Task-level synthesis
Trajectory-level synthesis
Feedback-level synthesis
Environment-level synthesis
We also review quality control, learning frameworks, synthetic evaluation data, and applications across software engineering, agentic search, AI for science, social good, and AI safety/security.
Links:
ResearchGate: [https://www.researchgate.net/publication/406488336_Scaling_LLM_Agent_Learning_with_Data_Synthesis_A_Comprehensive_Survey](https://www.researchgate.net/publication/406488336_Scaling_LLM_Agent_Learning_with_Data_Synthesis_A_Comprehensive_Survey)
OpenReview: [https://openreview.net/forum?id=pQYwkpYmLy](https://openreview.net/forum?id=pQYwkpYmLy)
arXiv: under moderation, link coming soon
#LLMAgents #DataSynthesis #AgentLearning #LLM #Survey
authored 3
papers about 1 month ago
EvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification
Paper • 2604.01687 • Published • 8
MOSAIC: Module Discovery via Sparse Additive Identifiable Causal Learning for Scientific Time Series
Paper • 2605.05524 • Published • 1
Verifiable Rewards Beyond Math and Code: Lightweight Corpus-Grounded Process Supervision for Factual Question Answering
Paper • 2605.29648 • Published • 10
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