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arxiv:2605.10781

Rebellious Student: Reversing Teacher Signals for Reasoning Exploration with Self-Distilled RLVR

Published on May 11
ยท Submitted by
JeonghyeKim
on May 12
Authors:
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Abstract

RLRT enhances self-distillation by reinforcing successful student decisions that deviate from teacher predictions, enabling more effective exploration in reinforcement learning via self-reward.

AI-generated summary

Self-distillation has emerged as a powerful framework for post-training LLMs, where a teacher conditioned on extra information guides a student without it, both from the same model. While this guidance is useful when the student has failed, on successful rollouts, the same mechanism instead overwrites the student's choices and suppresses it's own reasoning. Therefore, we propose reading the original self-distillation signal in reverse: when the student succeeds along a path the teacher would not have predicted, these tokens reflect its self-driven reasoning. Building on this, we propose RLRT (RLVR with Reversed Teacher), which augments GRPO by reinforcing these tokens on correct rollouts. We interpret this as a new form of exploration in RLVR: not uniform diversity, but valuable exploration grounded in the student's own success. Across base, instruction-tuned, and thinking-tuned Qwen3 checkpoints, RLRT substantially outperforms self-distillation and exploration-based baselines, establishing information asymmetry as a new, principled design axis for RLVR.

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Paper submitter

Enough with the obedient student. Time to rebel ๐Ÿง‘โ€๐ŸŽ“โšก

So far, on-policy self-distillation has pulled the student toward the teacher. But what if we force the student to follow the teacher even on paths it already got right? โ†’ Its own reasoning gets erased ๐Ÿง ๐Ÿ’จ

We introduce RLRT (RLVR with Reversed Teacher) ๐Ÿ”„. Instead of pulling the student toward the teacher, we amplify the tokens where the student diverged from the teacher (who has seen a correct solution) and still reached the correct answer. These tokens depart from one correct path yet remain verified, making them both self-driven and valuable exploration. ๐ŸŒŸ

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