How Large-Scale Post-Training Is Reshaping the Boundaries of Model Capabilities

1. From Parameter Scale to Training Paradigm: Two Main Threads in LLM Capability Improvement

Over the past two years, the evolution path of large language model capabilities has become increasingly clear, and the industry generally summarizes it into two interconnected stages: pre-training and post-training.

The core goal of the pre-training stage is to enable a model to build broad world knowledge and language understanding. Its main approaches span three dimensions: scaling up model parameters, optimizing model architecture design, and improving the scale and quality of training data. When parameter counts crossed into the trillion level, the emergent abilities demonstrated by models became a focal point of industry discussion for quite some time.

The post-training stage focuses on transforming the general capabilities acquired during pre-training into specialized abilities that can be reliably produced, verified, and adapted to specific tasks. Typical methods include supervised fine-tuning and reinforcement learning. Especially in the past year or two, large-scale reinforcement learning has moved from academic exploration to engineering deployment, becoming a key path for improving model reasoning and complex task handling.

Understanding the division of labor between these two threads is a prerequisite for understanding the current competitive landscape of model capabilities. Pre-training determines the “ceiling” of a model’s capabilities, while post-training largely determines how much of that ceiling can be released in real-world tasks.

2. Increased Post-Training Investment: From “Can Answer” to “Can Execute”

A notable recent trend is that some models have significantly increased their investment in post-training during version iterations. This investment is not simply about stacking more data, but is centered on a clear objective: extending the capabilities a model has accumulated in specific domains (such as coding) to more professional tasks that can be actually executed and repeatedly verified.

The significance of this shift can be understood through the differences in task nature.

Most past training tasks belonged to the “output equals result” category. For example, in solving a math problem, the model produces an answer, and the training system can determine correctness by comparing it against a standard answer. The reward signal is clear and cheap to obtain.

But real-world professional tasks, especially those involving engineering implementation, often cannot be judged by “glancing at the answer.” A complex programming task may require modifying multiple code locations, running multiple rounds of tests, handling dependencies, and fixing cascading errors. The quality of task completion can only be verified in a real execution environment.

This leads to a core proposition in post-training competition: improving model capabilities may increasingly depend on how many high-quality environments a model can repeatedly experiment in and obtain reliable feedback from.

3. Infrastructure for Large-Scale Agent RL: Why Execution Environments Are Critical

To understand the difficulty of deploying large-scale reinforcement learning on professional tasks, it is necessary to first examine the infrastructure it depends on. The following technical components play a key role in large-scale Agent RL. They may sound mundane, but none can be omitted.

3.1 Execution Environments and Sandboxes

In traditional training, the interaction between model and environment is lightweight: input a question, output an answer, and the environment returns a scalar reward. But in professional tasks, the environment itself is a complete execution system.

For coding tasks, for example, the model needs to be able to:

• Read and modify code files
• Run test cases
• Capture runtime errors and test failure information
• Continue modifying based on feedback
• Repeat the above process over multiple rounds

This means the training system must provide a real execution environment for the model. This environment needs to be isolated, controllable, reproducible, and capable of handling high-frequency interactive operations. Sandbox technology becomes a core component of the infrastructure here.

3.2 Large-Scale Scheduling Systems

If only training a single task, building an execution environment is not difficult. But the goal of large-scale reinforcement learning is to train thousands of tasks simultaneously.

This imposes strict requirements on the scheduling system:

• Dynamically allocate computing resources to different tasks
• Manage task queuing, execution, timeouts, and recycling
• Ensure isolation between tasks to avoid interference
• Maintain stable throughput under high concurrency

The efficiency of the scheduling system directly determines the number of training samples a model can obtain per unit of time, which in turn affects overall training efficiency.

3.3 Task Quality and Feedback Reliability

The effectiveness of reinforcement learning depends heavily on the quality of reward signals. If reward signals contain noise or bias, the model may learn incorrect policies.

In professional tasks, the sources of reward signals are more complex. They may come from:

• Test case pass rates
• Output from static analysis tools
• Correctness judgments of runtime behavior
• Final state after multiple rounds of execution

Therefore, the design quality of the tasks themselves, the rigor of the evaluation logic, and the stability of feedback signals together form the guarantee of post-training effectiveness. The value of a high-quality task set and a reliable evaluation mechanism is no less than that of the model’s own scale.

4. A Simplified Training Loop Illustration

To more intuitively understand how large-scale Agent RL operates, a piece of pseudocode can abstract its core loop:

for task in task_pool:
    env = create_sandbox(task)
    state = env.reset()
    for step in range(max_steps):
        action = model.generate(state)
        next_state, reward, done = env.execute(action)
        trajectory.append((state, action, reward))
        state = next_state
        if done:
            break
    # Compute final reward based on multi-round execution results
    final_reward = evaluate(trajectory, task.judge)
    model.update(trajectory, final_reward)
    env.cleanup()

This pseudocode omits a great deal of engineering detail, but reveals several key points:

1. Each task executes in an independent sandbox, ensuring isolation
2. The model interacts with the environment over multiple rounds, rather than a single output
3. Rewards come from the final execution result, not simple judgments of intermediate steps
4. Resources are recycled after task completion, freeing space for the next batch

When the task pool reaches thousands or tens of thousands in scale, every step of the above loop requires engineered scheduling and optimization.

5. The Second Half of Post-Training Competition: Environment, Feedback, and Scale

Taken together, the competitive focus in the post-training field is shifting.

In the pre-training era, the core competitive variables were parameter scale, data scale, and compute reserves. In the post-training era, especially when targeting executable and verifiable professional tasks, the core competitive variables are shifting toward:

• Environment quality and scale: Whether enough real, diverse execution environments can be built
• Feedback reliability and density: Whether stable, accurate, fine-grained reward signals can be obtained during task execution
• Scheduling system efficiency: Whether stable training throughput can be maintained under high concurrency
• Task set design quality: Whether the complexity and edge cases of real-world scenarios can be covered

These variables are interrelated and together determine how far a model can go in the post-training stage.

For developers, the practical significance of understanding this trend is: when evaluating a model’s performance on professional tasks, one should not only look at its parameter scale or pre-training data volume, but also pay attention to the environmental assets and feedback mechanisms it has accumulated during post-training. The boundaries of model capabilities are shifting from “what it knows” to “what it can reliably accomplish.”

For enterprises building intelligent applications, this trend also points to an important direction: in vertical domains, high-quality execution environments and verification mechanisms may become scarcer resources than the models themselves. Those who can build and leverage these resources more efficiently are more likely to gain an advantage in deploying professional tasks.