general family · filed under post-training
SFT–RL–On-Policy Distillation Pipeline
A post-training recipe ordered as supervised fine-tuning, reinforcement learning, and on-policy distillation, with no stated algorithmic modification.
- source
- 1
- model
- 1
- lab adopt it
- 1
- strongest
- used
How sources treat it
One count per evidence span, weakest treatment to strongest.
used 1
Documented in
Evidence
1 span quoted from the sources, strongest treatment first.
The post-training recipe follows the standard SFT → RL → on-policy distillation (OPD) paradigm without algorithmic modifications.
usedpost trainingin DeepSeek-V4.1-FlashDeepSeek
Filed alongside
Other methods under post-training :: policy distillation.
Multi-Teacher On-Policy DistillationOn-Policy DistillationPrefix-Conditioned On-Policy DistillationMulti-Prefix Multi-Teacher On-Policy DistillationSpecialist DistillationAutonomous Student RolloutsLarge-Scale On-Policy DistillationOn-Policy Cross-Stage DistillationAsynchronous Multi-Teacher On-Policy DistillationBehavior–Proximal Policy DecouplingDistillation Fine-Tuning on MiMo-Generated DataDistillation for Post-Training Data GenerationIcePop Token-Level Loss MaskingModel DistillationMulti-Objective Policy DistillationOff-Policy DistillationPer-Token On-Policy Distillation RewardTeacher-Trajectory and SFT-History Reuse