Decoding Looped Transformers Better for (Almost) Free
Paper • 2610.02185 • Published • 46
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POISE: Position-Aware Undetectable Skill Injection on LLM Agents
Latent Thoughts Tuning: Bridging Context and Reasoning with Fused Information in Latent Tokens
neosknight
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Latent Thoughts Tuning: Bridging Context and Reasoning with Fused Information in Latent Tokens
Paper • 2602.10229 • Published • 5
Verifiable Rewards Beyond Math and Code: Lightweight Corpus-Grounded Process Supervision for Factual Question Answering
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SPA: Towards A Computational Friendly Cloud-Base and On-Devices Collaboration Seq2seq Personalized Generation
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ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis
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RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback
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MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models
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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
Shichengf
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paper 4 months ago
ZhishanQ
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paper 4 months ago
Shichengf
authored 2
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Shichengf
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