Creating smaller, specialized models for your domain-specific agents is the future, and we’ve been prepping for the movement at Scale AI I’m excited to share the latest advancements we’ve made on Reinforcement Learning (RL) for enterprises! A few months ago, we shared why RL matters for the enterprise. Today, we’re sharing what’s next: results and learnings from applying our post-training RL stack with two key enterprise clients, and how we were able to achieve state of the art results including a 4B model that was able to surpass GPT-5. Through our experiments, we’ve consistently found that four factors are critical for RL: 1️⃣ High-quality data that captures the complexity of real enterprise workflows 2️⃣ Robust environments and stable training infrastructure 3️⃣ Rubrics, evals, and rewards specific to your problem 4️⃣ A strong model prior to elicit the right behaviors efficiently These are exactly what Scale’s platform and expertise bring to the enterprise. Check out our blog, where we dive into what we learned from each of these factors including ablations on data quality, tool-design intricacies, keys to a stable training infrastructure, and even some fun reward-hacking cases. You can find the blog here: https://lnkd.in/gyTk2RAW Special shout-out to Jerry Chan, Vijay S Kalmath, George Pu, and many others for the hard work to make this happen. If you’re an enterprise interested in learning how Scale can bring RL to your hardest domain-specific tasks, please reach out. And if you’re a researcher interested in making your algorithmic breakthroughs actually matter to business-driving outcomes, I’m hiring across many fun research roles!
Enterprise RL training is powerful but costly. Before RL, evaluate whether a strong retrieval + prompt + tool orchestration pipeline might deliver 80% of the gains with much lower cost.
Adrian Munoz
2w
Really cool to see real results behind the “smaller, specialized models” story instead of just hype! In those enterprise RL runs, did data quality or reward design end up moving the needle more when you were trying to beat the larger base models?
Sam Denton and the Scale AI team just delivered a masterclass. The shift toward smaller, specialized models is no longer a prediction — it’s the operating reality of enterprise AI. RL + high-fidelity data + stable infra + domain-specific rubrics = state-of-the-art. A 4B outperforming GPT-5? That’s a signal. The age of generic models is closing. The era of precision AI just opened.
Absolutely true. Every employee needs an orientation to their new work. RLHF gives you the chance to inform the model how to handle complexity YOUR way. You can scale it down to a single system prompt, sure...give your bot persona-level rules, sure, but RLHF gives context to the rules for tough edge cases. Worth the investment.
Don Miller
3w
My favorite model is the MLB model vs all of the all powerful all purpose models....the reason it is such a fun example is that 6+4+3 = 13 - All powerful all purpose 6+4+3 = 2 - MLB mdoel, because if you kept scorebook you know.... :) Thanks for sharing on small domain specific models!
Saba Asghar
4w
Impressive results. The focus on data quality and tailored reward design really highlights what makes enterprise RL succeed.
GRAVEIENS AI
3w
Domain-specific RL is clearly the path forward! A 4B model beating GPT-5 absolutely shows the power of specialized, focused expertise.
Mike Page
4d
I can do rlhf for domain specific tasks!
Roles: MLRE, Agents - https://scale.com/careers/4625344005 ML Systems RE, Agent Post-training - https://scale.com/careers/4625341005 MLRE, Agent Data Foundation - https://scale.com/careers/4625345005 Staff MLRE, Agent Post-training - https://scale.com/careers/4625337005