GLoRA replaces raw factor averaging with gauge-aware aggregation in a consensus subspace estimated from client projectors, enabling consistent low-rank federated LoRA under heterogeneity.
Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks
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SEIF creates a self-reinforcing loop in which an LLM alternately generates increasingly difficult instructions and learns to follow them better using reinforcement learning signals from its own judgments.
citing papers explorer
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Beyond Factor Aggregation: Gauge-Aware Low-Rank Server Representations for Federated LoRA
GLoRA replaces raw factor averaging with gauge-aware aggregation in a consensus subspace estimated from client projectors, enabling consistent low-rank federated LoRA under heterogeneity.
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SEIF: Self-Evolving Reinforcement Learning for Instruction Following
SEIF creates a self-reinforcing loop in which an LLM alternately generates increasingly difficult instructions and learns to follow them better using reinforcement learning signals from its own judgments.