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This work proposes minimal-edit corrective on-policy distillation, a method that preserves on-policy supervision while avoiding full-response imitation, and improves perception-oriented performance, particularly low-level geometric perception, and transfers to external mathematics and perception benchmarks.
ReFlux is introduced, a learnable feedback graph that dynamically selects and composes increment-carrying routes that establishes ReFlux as an efficient paradigm for unlocking the latent computational potential of LLMs, allowing them to revisit the same tokens with fresh representations.
This survey establishes workload-level boundaries and connects system architecture, competence acquisition, and evaluation through a seven-dimensional terminal competence profile, and provides a unified basis for studying terminal-mediated agency across software engineering and emerging application domains.
Xiao-Robotics-1 serves as a strong robot foundation policy that can be efficiently fine-tuned on complex, dexterous tasks with high data efficiency and across multiple simulation benchmarks, Xiaomi-Robotics-1 outperforms state-of-the-art methods.
Modality-Informed Reciprocal Reasoning Optimization (MIRROR), a reinforcement learning approach for improving multimodal reasoning via self supervision, is developed and improves over standard RL and yields more accurate and consistent behavior across modalities.
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