As we’re wrapping 2025, I’m excited to share our recent AI at Meta paper 🚀 : Scaling Reinforcement Learning for Content Moderation with LLMs (https://lnkd.in/gBRKh9EC) — focused on RL post-training as a practical lever for turning general-purpose LLMs into specialized, policy-aligned classifiers. We demonstrate: 👉 Sigmoid-like scaling behavior: performance improves smoothly and predictably as we scale training tokens, rollouts, and compute—then gradually saturates. 👉 Strong label efficiency: RL can match strong supervised fine-tuning baselines with far fewer labeled examples, which matters in real moderation settings where expert labels are scarce and expensive. 👉 A practical recipe that works in practice: reward shaping (accuracy + format + length + rubric), reflection-aided prompting, and Monte-Carlo score aggregation to stabilize training and improve reliability. This is the first paper in a series on scaling RL post-training for real-world LLM applications—spanning content understanding, recsys, and agentic systems. If you’re working along similar lines, I’d love to compare notes 👀 .
Amit B.
3mo
Great to see you Hamed Firooz working on a really tough and ambiguous problem - content moderation. Congrats Arpit Mittal
Imed Zitouni
3mo
Congrats to the team on this great work! 💝 🚀 🎉 🎊 It shows how we are leading the way in scalable, policy-driven content moderation—making our platforms safer and more inclusive for everyone. The advances in RL not only boost accuracy and efficiency, but also help us tackle real-world challenges where expert data is limited. 🤖 It’s exciting to see our research deliver actionable insights for industrial-scale moderation, and the impact it’s having on the broader community. 🌍 We’re continuing to innovate and grow in this space, with more cutting-edge solutions on the horizon. If you’re interested in impactful AI work, stay tuned for more exciting developments from our team! 🚀 #AI #ContentModeration #RL #Meta
Gary Li, Ph.D.
3mo
Really proud of the team’s research applying state-of-the-art reinforcement learning to the highly challenging content moderation domain.
E. Yi Liu
3mo
Just realize that you are back at Meta!
The sigmoid scaling curve you observed is the real insight here. We've seen similar patterns in inference workloads, diminishing returns once you hit the efficiency plateau. The label efficiency angle (matching SFT with fewer examples) is huge for content moderation where expert labels cost $50-100/hour at scale. What I'm curious about: did you see any tradeoffs between sample efficiency and calibration quality when pushing the RL reward shaping boundaries?