How to Build Custom Reasoning Agents with a Fraction of the Compute

Why It Matters

RLSD gives enterprises a cost‑effective, privacy‑preserving way to fine‑tune reasoning models on proprietary data, accelerating AI adoption without massive GPU budgets.

Key Takeaways

• RLSD outperforms RLVR and OPSD, achieving 56.18% average accuracy.
• Converges twice as fast, needing half the training steps.
• Requires only one extra forward pass, minimal compute overhead.
• Eliminates need for large external teacher models or annotated traces.
• Works with any verifiable reward signal, preserving enterprise data privacy.

Pulse Analysis

Training large‑scale reasoning models has long been a bottleneck for most enterprises. Traditional reinforcement‑learning‑with‑verifiable‑rewards (RLVR) offers only a binary signal at the end of a multi‑step reasoning trace, leaving the model blind to which intermediate tokens actually contributed to success. On‑policy distillation improves feedback granularity by pairing a small student with a massive teacher, but the duplicated model doubles GPU usage and forces identical vocabularies, limiting cross‑modality or multilingual deployments. Self‑distillation attempts to sidestep the teacher cost, yet privileged‑information leakage forces the student to mimic the teacher’s phrasing, causing performance to plateau and eventually degrade.

The newly proposed Reinforcement Learning with Verifiable Rewards and Self‑Distillation (RLSD) separates the direction of learning—provided by the reliable RLVR reward—from the magnitude of updates, which is derived from a token‑level self‑teacher assessment. By repurposing the model’s own logits to allocate credit across each reasoning step, RLSD delivers dense feedback without an external teacher or handcrafted annotations. In head‑to‑head tests on the Qwen3‑VL‑8B model, RLSD achieved a 56.18 % average accuracy across five visual‑reasoning benchmarks, outpacing the base model by 4.69 % and delivering roughly a two‑fold speedup in convergence.

For businesses, RLSD translates into a practical path to custom reasoning agents that run on modest GPU clusters while staying within corporate firewalls. The framework only requires a verifiable reward—such as a code compiler, SQL executor, or math checker—making it compatible with existing internal datasets like compliance manuals or ticket logs. Integration is lightweight, often a few lines of code in open‑source RL stacks, and the approach avoids sending proprietary data to third‑party APIs. As enterprises seek to embed trustworthy, domain‑specific reasoning into their workflows, RLSD offers a scalable, cost‑effective solution that bridges the gap between research‑grade models and real‑world deployment.