RAFT improves domain accuracy by 23.2% over standard SFT while recovering 18.2% and 10.2% relative performance on MS-Bench and IFEval through refined supervision and trajectory-preserving distillation.
MSSR : Memory-aware adaptive replay for continual LLM fine-tuning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
XekRung achieves state-of-the-art performance on cybersecurity benchmarks among same-scale models via tailored data synthesis and multi-stage training while retaining strong general capabilities.
citing papers explorer
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RAFT: Data Refinement and Adaptive Distillation for Domain Fine-Tuning with Alleviated Forgetting
RAFT improves domain accuracy by 23.2% over standard SFT while recovering 18.2% and 10.2% relative performance on MS-Bench and IFEval through refined supervision and trajectory-preserving distillation.
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XekRung Technical Report
XekRung achieves state-of-the-art performance on cybersecurity benchmarks among same-scale models via tailored data synthesis and multi-stage training while retaining strong general capabilities.