MLS-Bench (Agent) - Reinforcement Learning: leaderboard

Metric: Mean normalized task score (0-100): each metric is anchored so the worst reproduced human baseline scores 0 and the best 50 (a metric's theoretical optimum maps to 100), metrics are averaged with human weights within a setting and by geometric mean across at least three generalization settings, over the 13 MLS-Bench tasks of the Reinforcement Learning area; Agent protocol: the model's final submission after at most 20 actions including 3 test calls; high reasoning effort with a 10,000-token thinking budget, web search disabled, fixed seeds; higher is better. Source: arxiv.org. Saturation forecast: Around August 2028. 5 models tracked.

Top models

Interactive version: theaggregate.ai/benchmark?slug=mls-bench-agent-reinforcement-learning · How It Works · Data refreshed daily, snapshot 2026-10-07.