Modular AI systems assembled from contributed small models outperform monolithic LLMs by up to 15.4% on 15 tasks including reasoning and factuality while showing emergent problem-solving and benefits from contributor diversity.
The hallucination tax of rein- forcement finetuning.arXiv preprint arXiv:2505.13988
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MoCo supplies a unified library of 26 collaboration strategies and benchmarks demonstrating average outperformance over single models in 61 percent of (model, data) pairs.
JTS trains reasoning models via supervised warm-up and missing-premise RL to make an explicit answerability commitment that triggers early termination on unanswerable inputs, raising Abstention@Detection near saturation.
The paper reviews conceptual foundations, methodological innovations, effective designs, critical challenges, and future directions for LLM-based Agentic Reinforcement Learning.
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Scaling Participation in Modular AI Systems
Modular AI systems assembled from contributed small models outperform monolithic LLMs by up to 15.4% on 15 tasks including reasoning and factuality while showing emergent problem-solving and benefits from contributor diversity.
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MoCo: A One-Stop Shop for Model Collaboration Research
MoCo supplies a unified library of 26 collaboration strategies and benchmarks demonstrating average outperformance over single models in 61 percent of (model, data) pairs.
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Bridging the Detection-to-Abstention Gap in Reasoning Models under Insufficient Information
JTS trains reasoning models via supervised warm-up and missing-premise RL to make an explicit answerability commitment that triggers early termination on unanswerable inputs, raising Abstention@Detection near saturation.
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Rethinking Agentic Reinforcement Learning In Large Language Models
The paper reviews conceptual foundations, methodological innovations, effective designs, critical challenges, and future directions for LLM-based Agentic Reinforcement Learning.