Nvidia’s SoL-Pi system cuts coding agent token usage nearly in half by optimizing the harness

A new Nvidia paper describes a system that automatically optimizes the control layer of coding agents, known as the harness. Token usage drops by almost half while performance stays roughly the same, according to the researchers.

The longer AI agents work unsupervised, the more expensive they get. Single predictions turn into long chains of reasoning, tool calls, and feedback loops, and token usage balloons along the way. A new study from Nvidia researchers tackles these costs not at the model level but at the harness, the control layer between the model and its environment used by systems like Codex, Claude Code, or OpenClaw. The harness controls how an agent sees states, runs actions, and processes feedback. Most efficiency methods so far have focused on cutting the cost per token through faster attention kernels and serving infrastructure, model compression like quantization, or swapping in cheaper models. AI explores 152 directions to find leaner control logic Optimizing the harness is hard in practice because tool usage, context management, verification, and abort logic are all tightly coupled. A change that saves tokens in one place can trigger errors elsewhere or just push costs into a later phase. Typically, humans sift through long execution traces and translate recurring failure patterns into code. The system, called SoL-Pi, automates that work. A research agent watches another agent’s traces, proposes changes, and tests them in prepared environments. Capability and efficiency checks determine which candidates survive. The approach draws on recursive self-improvement, according to the authors. Across 535 executable environments, the system explored 152 directions, including 495 tasks derived from GitHub issue-pull-request pairs and 40 synthetic test cases. In total, the process generated more than 3,000 runs and over 60,000 agent-environment interactions. According to the researchers, this scale shows how broadly the system searched, but more search doesn’t automatically yield better results.