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

The AI Reasoning Revolution: How Enterprises Can Build Smarter Models Without Breaking the Bank

For years, the dream of training AI systems capable of deep, human-like reasoning has been tantalizingly out of reach for most organizations. The computational demands are staggering—training a single advanced reasoning model can cost millions in cloud infrastructure and require GPU clusters rivaling those of national research labs. But what if you could achieve comparable results with a fraction of the resources? A breakthrough from researchers at JD.com and academic collaborators is turning that possibility into reality, offering a new path forward for enterprises eager to harness AI reasoning without the astronomical price tag.

At the heart of this innovation lies a clever fusion of two established techniques—reinforcement learning and knowledge distillation—into a single, efficient training framework known as Reinforcement Learning with Verifiable Rewards with Self-Distillation (RLSD). This hybrid approach not only improves model performance but dramatically reduces the computational burden, making custom reasoning agents accessible to a much broader range of organizations.

The Hidden Cost of Traditional AI Training

Most enterprises attempting to build reasoning-capable AI today face a brutal trade-off: either invest heavily in massive infrastructure or settle for underperforming models. The standard method, Reinforcement Learning with Verifiable Rewards (RLVR), relies on trial-and-error learning where an AI generates a reasoning chain—sometimes thousands of tokens long—and receives a single binary reward (correct or incorrect) at the end.

This creates what researchers call the “signal density problem.” Imagine a student writing a 10-page essay and receiving only a final grade of “A” or “F” with no comments on grammar, logic, or structure. They might eventually learn to pass, but they’ll never understand why certain arguments worked or failed. Similarly, in RLVR, every token in a reasoning trace receives identical credit, regardless of whether it was a critical deduction or a redundant filler phrase.

This sparse feedback slows learning, increases training time, and often leads to models that “game the system”—producing correct answers through flawed logic that just happens to pass the final check. Worse, because the reward signal is so delayed and coarse, models struggle to refine their internal reasoning processes, limiting their ability to generalize to new problems.

The Teacher-Student Dilemma: Why Distillation Isn’t the Silver Bullet

To address the feedback gap, some teams turn to On-Policy Distillation (OPD), a technique inspired by the age-old educational model of a teacher guiding a student. In OPD, a smaller “student” model learns by mimicking the output of a larger, more capable “teacher” model. For every training example, the student compares its response token-by-token with the teacher’s, receiving rich, granular feedback throughout the entire reasoning process.

This method excels at teaching nuanced logic and step-by-step problem-solving. However, it comes with a steep price: computational overhead. The teacher model must remain active and fully loaded in memory throughout the entire training cycle, effectively doubling the GPU requirements. For a mid-sized enterprise, this could mean renting dozens of high-end A100 or H100 GPUs for weeks—costing tens or even hundreds of thousands of dollars.

This creates a paradox: the method that offers the best learning signal is often too expensive to use at scale. As Chenxu Yang, co-author of the RLSD study, put it: “You have to keep a larger teacher model resident throughout training, which roughly doubles your GPU footprint.” For most companies, that’s a non-starter.

RLSD: The Best of Both Worlds

Enter Reinforcement Learning with Verifiable Rewards with Self-Distillation (RLSD)—a training paradigm that merges the strengths of RLVR and OPD while sidestepping their weaknesses. RLSD operates in two synchronized phases: reinforcement learning for outcome-based rewards and self-distillation for internal feedback refinement.

During training, the model generates reasoning traces and receives binary rewards from a verifier, just like in RLVR. But instead of stopping there, RLSD introduces a self-distillation loop: the model uses its own previous outputs as pseudo-teachers. It compares current reasoning steps with earlier, higher-confidence responses, creating a continuous feedback cycle that highlights which logical moves were effective and which were noise.

This self-referential learning allows the model to internalize high-quality reasoning patterns without needing an external teacher model. The result? Granular feedback across the entire reasoning chain—without the massive GPU overhead.

In experiments, models trained with RLSD consistently outperformed those trained with pure RLVR or OPD on benchmarks like GSM8K (math word problems) and MATH (advanced mathematical reasoning). More importantly, they achieved these gains with significantly lower computational costs—sometimes using just a fraction of the GPUs required by traditional methods.

How Self-Distillation Works in Practice

Self-distillation in RLSD isn’t just about copying past outputs. It’s a dynamic process where the model evaluates its own reasoning trajectories over time. For example, when solving a complex algebra problem, the model might generate multiple reasoning paths. RLSD identifies which paths led to correct answers and which diverged into errors. Then, it uses those successful paths as “golden examples” to guide future reasoning.

This creates a virtuous cycle: the better the model gets, the higher quality its self-generated feedback becomes. Over time, the model learns not just what the right answer is, but how to arrive at it through sound logic.

Unlike OPD, where the teacher model is static and external, RLSD’s self-distillation is adaptive and internal. This means the model can continuously improve without requiring additional infrastructure. It’s like a student who reviews their own past exams to identify patterns in their mistakes—leading to faster, more sustainable learning.

Real-World Impact: From Research Labs to Enterprise Applications

The implications of RLSD extend far beyond academic benchmarks. For enterprises, this technique opens the door to building custom reasoning agents tailored to specific domains—such as financial forecasting, supply chain optimization, or legal document analysis—without prohibitive costs.

Consider a logistics company trying to optimize delivery routes under dynamic constraints. A reasoning agent trained with RLSD could learn to balance fuel costs, traffic patterns, delivery windows, and vehicle capacity—reasoning through thousands of variables in real time. With traditional methods, training such a model might require a dedicated GPU cluster. With RLSD, the same model could be trained on a modest setup, slashing both cost and deployment time.

Another example is in healthcare, where AI reasoning models can assist in diagnosing rare conditions by analyzing patient histories, lab results, and medical literature. RLSD enables hospitals and clinics to fine-tune models on local data without relying on expensive cloud resources, preserving patient privacy while improving diagnostic accuracy.

The Future of Efficient AI Reasoning

RLSD represents more than just a technical improvement—it’s a shift in how we think about AI development. By decoupling high performance from massive compute, it democratizes access to advanced reasoning capabilities. Smaller teams, startups, and even non-tech enterprises can now experiment with custom AI agents that understand complex logic, make sound decisions, and adapt to new challenges.

As the technology matures, we can expect to see RLSD integrated into popular AI frameworks and cloud platforms, further lowering the barrier to entry. Researchers are already exploring variants that incorporate human feedback, multi-agent collaboration, and real-time learning—pushing the boundaries of what’s possible with limited resources.

The journey toward truly intelligent AI is long, but RLSD proves that innovation doesn’t always require more power—sometimes, it just requires smarter design. For enterprises ready to build the next generation of reasoning agents, the future is not only brighter but more affordable.

This article was curated from How to build custom reasoning agents with a fraction of the compute via VentureBeat