1 link tagged with all of: reinforcement learning + agents + on policy distillation + reasoning
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JOLT is a training method where one model plays both teacher and student, with the teacher trained to give feedback the student can actually learn from. The authors report gains in accuracy and token efficiency over GRPO on math, coding, and agentic tasks.
- Outcome-reward RL gives sparse feedback, and on long agentic tasks with rare successes that makes learning slow and sample-hungry.
- Privileged-information teachers (like a model that sees the verified solution) can be good at the task but poor at teaching it, since their guidance may exceed what the student can absorb.
- JOLT trains the teacher to be "reward-consistent," meaning its guidance pushes the student in the same direction as RL on the student's own reward. Reported gains over GRPO include +4.2pp on MATH, +3.0pp on LCBv6, and +9.9pp on AppWorld.