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Standard single-turn RL produces socially myopic agents, but dynamically scheduling rewards from early relationship-building to mid-dialogue task execution drives a 9.2 percentage point gain in multi-turn goal achievement.
HarnessLens boosts agent performance by up to 13.6% while slashing evaluation costs through smarter, behavior-aware verification.
MiniMax-M2 proves that massive parameter counts don't always translate to better agentic performance; strategic activation of a smaller subset can unlock frontier-level intelligence.
