Nvidia's SoL-Pi cuts coding agent token usage by nearly half
Nvidia researchers released a paper describing SoL‑Pi, a system that automatically optimizes the harness of coding agents. The harness sits between a model and its environment and controls how an agent sees states, runs actions, and processes feedback. According to the researchers, SoL‑Pi cuts token usage by almost half while keeping performance roughly the same.
Key points
- SoL‑Pi reduces coding agent token usage by 49 percent while maintaining 93.7 percent of Pi harness score.
- The system explored 152 directions across 535 environments, generating over 60,000 agent‑environment interactions.
- Four mechanisms—Action Fusion, Online Context Compact, ObservationPack, Evidence‑Preserving Reducer—each cut token usage by merging steps, trimming context, summarizing outputs, or routing logs to cheaper models.
The study explored 152 directions across 535 executable environments, including 495 tasks from GitHub issue‑pull‑request pairs and 40 synthetic test cases. It generated more than 3,000 runs and over 60,000 agent‑environment interactions. Four mechanisms—Action Fusion, Online Context Compact, ObservationPack, and Evidence‑Preserving Reducer—each reduce token usage by merging steps, trimming context, summarizing outputs, or routing logs to cheaper models.
On EdgeBench’s 51 public tasks, the most efficient variant of SoL‑Pi used 49 percent fewer tokens and achieved 93.7 percent of Pi harness score. Users prioritizing performance can beat Pi’s score by 5.3 percent while still saving tokens. In dollar terms, the researchers estimate savings of $8.75 to $13.50 per hour versus native Codex and Claude Code harnesses, and $4.36 to $5.71 per hour versus Pi.
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Nvidia's SoL-Pi system cuts coding agent token usage nearly in half by optimizing the harness
The Decoder · 26 September 2026
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