How Post-Training Compute Unlocks the Potential of Ultra-Scale Models

1. A Paradigm Shift That Is Already Underway

Over the past few years, the capability growth of large models has been tightly coupled with pretraining scale: more parameters, more data, and more compute produced stronger models. But the marginal returns on this curve are shrinking—high-quality training corpora are running out, the compute cost of a single pretraining run has reached an enormous magnitude, and capability improvements are no longer growing linearly.

A notable new signal is emerging: the capability leaps of flagship models are increasingly coming from the post-training stage rather than from a brand-new, larger-scale pretraining run.

A recent flagship model update from a major cloud provider illustrates this logic. The model reuses the base version released earlier—no new, larger pretraining was performed. Instead, the team dramatically increased post-training compute, extended reinforcement learning to hundreds of downstream domains, and optimized the training framework, long-context inference, sandbox environments, and task scheduling systems. The result: clear progress in coding, agents, 3D modeling, and professional knowledge work.

Behind this lies a clear technical logic: the base model determines the “ceiling” of capability, while post-training determines how much of that capability is actually released. Once a base model reaches the multi-trillion-parameter range and its context window extends to the million-token scale, what limits real-world performance is often not what the model “knows,” but whether it can “consistently get things right in real tasks.”

2. Why Post-Training Has Become the Main Battleground

2.1 The Diminishing-Returns Dilemma of Pretraining

Pretraining is essentially massive next-token prediction. It gives models statistical knowledge of language, world knowledge, and basic reasoning ability. But once models enter the multi-trillion-parameter range, further pretraining expansion faces three practical constraints:

• Data constraints: The supply of high-quality, diverse training corpora is limited, and the returns from repeatedly training on the same data decay rapidly;
• Compute constraints: A single pretraining run consumes enormous compute, takes months to complete, and makes trial-and-error prohibitively expensive;
• Objective misalignment: Pretraining optimizes “average prediction accuracy,” not “task completion on specific tasks.”

In other words, pretraining makes a model “well-read,” but it does not guarantee the model will “do things right” in a specific scenario.

2.2 What Problem Does Post-Training Solve?

Post-training covers supervised fine-tuning, preference alignment, and reinforcement learning. Its core goal is to convert the base model’s “general potential” into “task capability.” In particular, reinforcement learning based on verifiable rewards allows a model to try repeatedly in real or simulated task environments and adjust its strategy based on outcome feedback.

The key to these methods: the reward signal comes from whether the task is actually completed, not from subjective human ratings of answer style. Whether code passes tests, whether a modeling script runs successfully, whether a document analysis result matches the ground truth—all of these can be verified automatically, which makes large-scale, sustainable reinforcement learning loops possible.

3. Scaling RL to Hundreds of Domains: The Engineering Challenges

“Extending reinforcement learning to hundreds of downstream domains” sounds simple, but in practice it is a complex systems engineering problem.

3.1 Heterogeneous Task Environments

Tasks in different domains require different execution environments:

• Coding tasks need isolated containers where code can run and tests can execute;
• 3D modeling tasks need environments that can invoke modeling tools and rendering engines;
• Financial document analysis needs toolchains that can parse structured and unstructured documents and validate numerical results;
• Multi-step software engineering tasks need to maintain state over long periods and support file system operations and version control.

This means post-training infrastructure must support heterogeneous sandbox environments: a unified scheduling interface, while still allowing each domain to customize its own toolset and validation logic.

3.2 Long-Context Inference Overhead

When the context window reaches the million-token scale, KV cache memory usage and attention computation costs rise significantly per inference. In a reinforcement learning loop, the model repeatedly reads long documents, large codebases, and long conversation histories, which places much higher demands on the inference engine’s memory management and batch scheduling.

Common optimization directions include:

• Paged KV cache management to reduce memory peaks for long sequences;
• Prefix cache reuse to avoid recomputing identical contexts across multiple samples;
• Dynamic batching and preemptive scheduling to improve GPU utilization.

3.3 Task Scheduling and Training Stability

Training hundreds of domains simultaneously means thousands of task instances may run in parallel every second. The scheduling system must handle:

• Priorities and quotas: Task durations vary dramatically across domains, so compute must be allocated sensibly;
• Failure isolation: An exception in one environment must not bring down the entire training run;
• Data feedback: Task results must be efficiently returned to the training side to close the loop.

A simplified scheduling flow can be expressed in the following pseudocode:

def training_loop(policy, task_pool, rollout_workers):
    while not converged:
        batch = task_pool.sample(num_tasks=config.batch_size)
        # Dispatch to heterogeneous sandboxes for parallel execution
        trajectories = rollout_workers.run(
            policy=policy,
            tasks=batch,
            timeout=config.task_timeout,
        )
        # Filter and score based on verifiable rewards
        valid = [t for t in trajectories if t.verified]
        rewards = [compute_reward(t) for t in valid]
        # Update the policy
        policy.update(valid, rewards)
        # Log metrics for monitoring and tuning
        metrics.log(valid, rewards)

This logic looks simple, but every line requires mature engineering support: sandbox lifecycle management, timeouts and retries, correctness validation of reward functions, and resource isolation between training and inference.

4. Three Dimensions of Capability Gains

Judging from publicly released benchmark results, the gains from post-training scaling concentrate in a few areas:

4.1 Real-World Terminal Coding Tasks

On a benchmark measuring real-world terminal coding tasks, the model scored 65.7%, surpassing several international flagship models released around the same period and trailing the best result by only 0.7 percentage points. What makes this type of benchmark distinctive: the tasks come from real development scenarios, and the model must actually modify code, run tests, and continue debugging based on error messages—rather than simply generating a piece of code that looks correct.

This confirms the earlier observation: capability gains increasingly depend on repeated attempts and feedback in real tasks.

4.2 Professional Document Analysis

On a financial document analysis benchmark, the model scored 64.4 points, achieving the top score on that test. These tasks require the model to locate key information in long documents, perform numerical computation and logical reasoning, and produce verifiable conclusions. The common thread with coding tasks: the results can be automatically verified, making them well-suited as training signal sources for reinforcement learning.

4.3 3D Modeling and Multi-Step Engineering

The model can also invoke professional tools such as modeling software, CAD, and game engines to complete complex 3D modeling, game scene development, and long-running, multi-step software engineering tasks. The challenges of this task category include:

• The steps are long, and an error at any intermediate step can cause overall failure;
• State must be maintained across steps;
• Tool calls require precise parameters and formats.

Consistently completing these tasks indicates substantive improvements in planning, tool use, and error recovery.

5. Implications for Developers and Enterprises

5.1 Reassess the “Base Model + Post-Training” Strategy

For most teams, training a multi-trillion-parameter base model from scratch is unrealistic. The more viable path: pick a sufficiently capable base model and focus engineering effort on post-training and task adaptation. This includes building domain-specific task environments, designing verifiable reward signals, and setting up stable scheduling and sandbox infrastructure.

5.2 Infrastructure Is the Ceiling of Post-Training

The bottleneck of post-training scaling is often not the algorithm but engineering capability. Training hundreds of task types simultaneously requires:

• Elastically scalable container and sandbox platforms;
• High-throughput, low-latency inference services;
• Unified task scheduling with monitoring and alerting;
• Reliable data storage and logging to support observability of the training process.

Mature product categories for these capabilities already exist on general-purpose cloud platforms, so teams can assemble them as needed instead of building everything from scratch.

5.3 The More Complex the Task, the More Compute It Consumes

A fact that is easy to overlook: the more complex the task, the more inference compute the training consumes. Complex tasks require longer trajectories, more attempts, and more frequent tool calls. This means the cost structure of post-training is fundamentally different from traditional fine-tuning, and compute budgets and resource planning need to be prepared in advance.

6. Conclusion

From “stacking pretraining” to “competing on post-training,” the logic of large model capability growth is undergoing a structural change. Parameter scale remains the foundation, but what truly determines whether a model performs reliably in real-world scenarios is the post-training stage: task design, environment construction, feedback loops, and engineering orchestration.

For developers and enterprises, this opens a new window of opportunity: you don’t need the largest base model—but with solid post-training engineering, you can fully unlock the capabilities of the base model you already have. And the core of that engineering capability lies in verifiable task environments, scalable sandbox infrastructure, and a reinforcement learning loop that can run sustainably.