Lightning OPD is an offline on-policy distillation method that matches standard OPD performance at 4x efficiency by enforcing teacher consistency between SFT and distillation phases.
UFT: Unifying supervised and reinforcement fine-tuning
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
WavAlign introduces an adaptive hybrid post-training recipe that makes reinforcement learning practical for spoken dialogue models by separating semantic preference updates from acoustic anchoring and regulating their mixture to yield better semantic quality and expressiveness.
citing papers explorer
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Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation
Lightning OPD is an offline on-policy distillation method that matches standard OPD performance at 4x efficiency by enforcing teacher consistency between SFT and distillation phases.
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WavAlign: Enhancing Intelligence and Expressiveness in Spoken Dialogue Models via Adaptive Hybrid Post-Training
WavAlign introduces an adaptive hybrid post-training recipe that makes reinforcement learning practical for spoken dialogue models by separating semantic preference updates from acoustic anchoring and regulating their mixture to yield better semantic quality and expressiveness.