According to AI at Meta on X, Meta is using reinforcement learning to train models to engage in test-time reasoning—letting them think before answering—while controlling cost via two levers: thinking time penalties to optimize token usage and multi-agent orchestration to improve answer quality and latency. As reported by AI at Meta, the thinking time penalty encourages shorter, more efficient chains of thought, reducing inference tokens and compute, while orchestration coordinates multiple specialized agents to boost accuracy and reliability at scale. According to AI at Meta, these techniques are designed to serve billions of users with efficient token budgets, suggesting enterprise opportunities in cost-aware reasoning, agent routing, and latency SLAs for production LLMs.