How to build custom reasoning agents with a fraction of the compute

2026-04-28 · Source: VentureBeat · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Advanced, extended

Summary

JD.com and academic researchers have introduced Reinforcement Learning with Self-Distillation (RLSD), a new AI training paradigm that addresses the resource-intensive nature of reasoning models. RLSD combines the reliable, sparse feedback of Reinforcement Learning with Verifiable Rewards (RLVR) with the granular, dense feedback of self-distillation, avoiding the computational overhead of On-Policy Distillation (OPD) and the "privileged information leakage" of On-Policy Self-Distillation (OPSD). Experiments with the Qwen3-VL-8B vision-language model on benchmarks like MMMU and MathVision showed RLSD achieved 56.18% average accuracy, outperforming RLVR by 2.32% and demonstrating a 2x convergence speedup. The method decouples update direction from magnitude, using verifiable rewards for direction and self-distillation for magnitude, providing fine-grained credit allocation without complex auxiliary networks or external teacher models. This approach significantly lowers barriers for enterprises to develop custom reasoning models.

Key takeaway

For AI scientists and machine learning engineers developing reasoning models, RLSD offers a compelling alternative to traditional methods. Your teams can achieve higher accuracy and faster convergence by adopting RLSD, especially for tasks with verifiable outcomes like code compilation or mathematical validation. This approach reduces computational costs and avoids issues like information leakage, making it feasible to build custom, high-performing models tailored to specific business logic without needing massive external teacher models.

Key insights

RLSD combines sparse, reliable reinforcement learning with dense self-distillation for efficient, accurate AI reasoning model training.

Principles

• Decouple update direction from magnitude in AI training.
• Reliable feedback is critical for update direction.
• Dense feedback improves update magnitude and fine-grained corrections.

Method

RLSD uses verifiable environmental feedback to determine the direction of model updates and repurposes a self-teacher's token-by-token assessment to determine the magnitude of those updates, distributing credit or blame across reasoning steps.

In practice

• Integrate RLSD for tasks with verifiable reward signals.
• Utilize proprietary internal data as "privileged information" for RLSD.
• Implement RLSD with minimal code changes in existing RL frameworks.

Topics

• Reinforcement Learning with Self-Distillation
• AI Reasoning Models
• Compute Efficiency
• Self-Distillation
• Verifiable Rewards

Best for: Computer Vision Engineer, AI Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, Director of AI/ML

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• Meet KARL: A Faster Agent for Enterprise Knowledge, powered by custom RL — Custom reinforcement learning can drastically improve enterprise agent performance while reducing costs.
• Captaining IMO Gold, Deep Think, On-Policy RL, Feeling the AGI in Singapore — Yi Tay — On-policy RL and self-consistency are key to advancing LLM reasoning and achieving AGI-like capabilities.

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Editorial summary, takeaway, and curation by AIssential. Original article published by VentureBeat.