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Matching papers

Macaron-V1: Towards Open Continual Learning with Self-Improvement and Mixture-of-LoRA

157 upvotes · 10 AUG 2026 · Mind Lab, Vin Bo, Asher Cai et al.

This paper introduces Macaron-V1, an open agent model family that enables continual learning and self-improvement in real-world environments, and explores its potential for collective intelligence. Practitioners might care about this work if they're interested in developing AI systems that can learn and adapt over time.

continual learningself-improvementmixture-of-LoRAagent modelsopen-source AI

FlowBalance: Verifier-Grounded Self-Improvement from On-Policy Reasoning Experience

52 upvotes · 3 SEP 2026 · Zixun Huang, Kishan Panaganti, Haitao Mi et al.

This paper introduces FlowBalance, a method that helps a reasoning model improve itself by learning from its own experiences, while avoiding overconfidence and focusing on the best solutions. Practitioners might care about this approach because it can lead to more accurate and diverse model performance.

reinforcement learningon-policy learningself-improvementverifierstrajectory balance