RLSD lets enterprises train custom AI reasoning models with far less compute and better stability

Published on 29 April 2026

Self-teaching can backfire by leaking hidden answers

A new training method called RLSD from JD.com and academic researchers aims to solve a common enterprise bottleneck: reasoning models are expensive to train and often get only sparse feedback. RLSD keeps reinforcement learning’s reliable direction while using self-distillation only for credit assignment, avoiding “privileged information leakage.” In tests, it beat baseline GRPO and standard OPSD with faster convergence.

RLSD improves reasoning training by separating learning direction from credit magnitude
It avoids OPSD’s privileged information leakage that can collapse reasoning over time
Results on multiple benchmarks show higher accuracy and about 2x faster convergence
Enterprises can start with verifiable rewards like code, math checks, SQL, or schema validators

#self distillation #reinforcement learning #reasoning models #ai training #enterprise ai

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RLSD lets enterprises train custom AI reasoning models with far less compute and better stability

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