NVIDIA Vera Rubin: Consider a professional athlete. What separates elite performers is what happens between games: continuous refinement, adjusting to new opponents, and sharpening skills based on what the last game exposed.
Agentic AI works the same way. Instead of answering a single prompt, it’s given a goal and has to keep adapting as environments shift, edge cases emerge, and tools change. Unlike a generative model responding to a prompt, an agentic model must plan, use different tools, and recover from problems it encounters mid-run.
That is the reason why post-training, the stage that optimizes a model after initial training on raw data, is no longer a one-time finishing step. It’s continuous because the environment that agentic models operate in shifts fast. The tools an agent uses can change week to week. Edge cases surface in production that no test set anticipated. Each deployment brings its own codebase, policies, and environment.
OBJECTIVES OF POST-TRAINING
Post-training cycles feed back from production as fresh issues arise. The computing load expands not because each run is heavier, but because the runs continue endlessly.
Agentic AI unveils a new compute pattern for post-training, making it the central workload of the agentic era and the primary driver of intelligence per dollar.
The core goals of post-training include maximizing intelligence per dollar by expanding the yield of every forward and backward pass in the continuous learning cycle. The forward pass — inference — is measured as cost per token. That means that every improvement to cost per token flows directly into intelligence per dollar.
Demystifying Agentic Post-Training
Post-training is where intelligence is developed. In pretraining, the model learns to predict the next token, which provides it fluency but not intelligence. Post-training is where it masters writing code, planning a multistep task, using a search tool, and recovering when something goes wrong. Inference is what comes after: the model working on the job, priced at cost per token.
Since there’s no fixed answer key to memorize, only rewards, the model improves through reinforcement learning (RL). When assigned a task, it produces an attempt, known as the forward pass, performing the same work it does on the job. The attempt is scored, and the lesson updates the model’s weights through the backward pass. Across millions of attempts, intelligence strengthens
Each step is compute demanding, and executing this loop at scale becomes an orchestration challenge: thousands of environments produce rollouts in parallel, rewards are validated, and updated weights flow back into training with accelerators fully engaged. NVIDIA NeMo open libraries, such as NeMo Gym for training environments and NeMo RL for distributed post-training, turn post-training from custom research code into repeatable infrastructure.
Why Intelligence per Dollar Expands Cost per Token
If inference acts as the revenue engine, post-training serves as the multiplier: the more capable the model becomes, the greater the value of every token delivered.
Cost per token is the core metric for the inference factory: the total expense of delivering one million tokens. Intelligence per dollar sits a level higher, addressing a different question: What does it cost to build a model worth serving, and keep it worth serving as its environment changes?
The two are nested, not competing. AI infrastructure that lowers costs per token also lowers the costs of every point of intelligence built into the model. And every point of intelligence built in raises the value of every token the inference factory serves.
In other terms, cost per token reflects operating efficiency, while intelligence per dollar reflects whether the investment in model capability is delivering returns.
Optimizing Intelligence per Dollar with Nemotron 3 Ultra Post-Training
NVIDIA Nemotron 3 Ultra is an open-weight, 550-billion-parameter mixture-of-experts (MoE) model that offers verifiable benchmarks and a fully disclosed post-training recipe run on NeMo RL. It achieved a 71.7% score on a standard real-world coding benchmark, SWE-bench verified, successfully producing fixes for roughly seven out of ten genuine software bugs from open-source projects, each validated against the project’s own tests.
The NVIDIA Blackwell platform lowers costs per run and makes the frequent post-training the agentic era demands economically viable. That intelligence is reaped across every token served.
The NVIDIA Vera Rubin platform pushes the trajectory further, training the largest models with one-fourth the GPUs of the Blackwell generation. It was co-engineered from end to end to optimize intelligence per dollar for the agentic post-training workload: more rollouts per iteration, more environments in active and post-training cycles that never cease.
POST TRAINING WORKFLOWS IN MOTION
Prime Intellect’s Lab continuously post-train frontier open models on NVIDIA Blackwell and employs NVIDIA Dynamo for inference orchestration. With Vera Rubin, Prime Intellect aims to scale reinforcement learning environments, generate more rollouts per iteration, and accelerate training‑to‑inference cycles to optimize intelligence per dollar for businesses.
Optimizing Sandbox Infrastructure with NVIDIA Vera
Prime Intellect has streamlined its sandbox infrastructure to integrate with NVIDIA Vera CPUs, enabling low‑latency and energy‑efficient reinforcement learning. Open‑source frameworks and models such as NVIDIA Nemotron and NVIDIA NeMo Gym are incorporated into its software stack. In comparison with alternative x86 architectures on realistic RL sandbox workloads,Prime Intellect observed that Vera delivers, on average, 30% higher throughput per CPU.
Perplexity’s RL post‑training stack operates asynchronously across hundreds of NVIDIA GPUs, featuring an RDMA‑based weight transfer engine that synchronizes trillion‑parameter models in under two seconds between training and inference nodes. The resulting post‑trained Qwen3 235B models are afterward implemented on NVIDIA GB200 NVL72 systems.”
CONCLUSION
Collective AI delivers post‑training as a service, spanning supervised fine‑tuning, reinforcement learning, and direct preference optimization. The service is delivered via a feature-rich application programming interface and software development kit that supports the full range of post-training on its AI Native Cloud platform. It has been operating on NVIDIA’s platform with optimized kernel libraries and is now preparing to harness the Vera Rubin platform next.
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