Unverified50% confidenceFactExact time
The core design of JOLT is to let the same model play both the teacher and student roles at once, with the teacher role specially trained to generate feedback signals that the student can exploit.
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10/24/2026
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UnverifiedJOLT's design eliminates the overhead of maintaining two sets of parameters and lets the teacher signal evolve in sync with the student policy, avoiding the teacher lagging behind the student's current capability level due to being frozen.70% similarVerifiedOnline policy distillation (OPD) has the student model generate its own training responses, which are then supervised by the teacher model.70% similarUnverifiedResearch teams from Perplexity and NVIDIA jointly proposed a post-training method called JOLT, which lets the same model play both the teacher and student roles and trains it with joint reinforcement learning.67% similarUnverifiedSelf-Distillation Policy Optimization (SDPO) provides dense token-level supervision signals to the student model through a feedback-conditioned "self-teacher".67% similarUnverifiedIn self-distillation, if the teacher is an EMA or synchronous variant of the student policy, each update of the student changes the teacher, causing the target signal to drift and turning training into chasing a moving target.64% similar
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