The New Competitive Paradigm in the Post-Training Era for Large Models

1. From Pretraining to Post-Training: The Twin Engines of Capability Improvement

The evolution of large model capabilities currently follows two main technical routes.

The first route is pretraining. Its core work includes scaling up model parameters, optimizing network architecture design, and continuously improving the scale and quality of training data. Over the past few years, parameter growth has brought significant gains in foundational capabilities — from hundreds of millions to hundreds of billions and even trillions of parameters, the emergent abilities displayed by models have repeatedly reshaped industry expectations. However, pretraining is facing the reality of diminishing marginal returns: scaling parameters demands massive computational resources, while the corresponding capability gains are gradually narrowing. At the same time, acquiring high-quality training data is becoming increasingly difficult, and the room for optimizing data cleaning and mixing strategies is steadily shrinking.

The second route is post-training. This stage encompasses a variety of techniques, including supervised fine-tuning and reinforcement learning. In the past one to two years in particular, large-scale reinforcement learning has become a critical path for improving model reasoning capabilities and handling complex tasks. Unlike pretraining, which emphasizes “knowledge injection,” post-training focuses more on “capability alignment” — teaching models to invoke existing knowledge in expected ways within specific task scenarios and to complete multi-step reasoning and decision-making.

A noteworthy trend is that once pretraining reaches a certain scale, the depth of post-training investment is becoming the key variable that differentiates actual model performance. The reason is straightforward: pretraining endows models with general foundational capabilities, while post-training determines whether those capabilities can be effectively activated and combined in concrete tasks.

2. The Core Challenge of Post-Training: From Static Evaluation to Dynamic Interaction

Traditionally, reinforcement learning in post-training has relied primarily on human feedback or static preference data for optimization. Take math problems as an example: after a model produces an answer, it can be checked against a reference solution to determine correctness. The reward signal is relatively clear, and the training pipeline is straightforward.

But when we shift the target toward more complex professional tasks — such as code generation, system debugging, and multi-step logical reasoning — the problem becomes far more complicated.

First, there is no longer a single correct answer. A programming problem may have multiple valid implementations. The evaluation criteria extend beyond “does it run” to include code quality, edge case handling, performance, and other dimensions.

Second, tasks require interaction with an environment. A model cannot complete a task with a single output. It needs to modify code, run tests, observe error messages, locate issues, and revise again — a process that may require multiple iterations.

Third, reward signal design becomes significantly harder. For a multi-step execution process, how do you reasonably attribute the success or failure of the final result to intermediate steps? How do you design dense rewards to guide the model progressively toward correct solutions? These are technical challenges that demand careful solutions.

This means the infrastructure requirements for post-training have undergone a fundamental shift: from “static datasets + offline training” to “dynamic environments + online interaction.”

3. Infrastructure Requirements for Large-Scale Agent Reinforcement Learning

To enable models to learn through repeated trial and error in complex tasks, the training system must build a complete set of infrastructure to support large-scale interaction. The names of these technical components may sound dry, but each directly determines whether training can scale and run reliably.

3.1 Execution Environments and Sandbox Systems

Models need controllable, reproducible execution environments to complete tasks. For example, in code generation scenarios, model-generated code must run in isolated sandboxes to prevent any impact on the training system itself. Sandboxes need the following properties:

• Isolation: Each task runs in an independent environment without interference from others.
• Reproducibility: Identical inputs should produce consistent environment states, ensuring reliable training signals.
• Fast reset: After a task completes, the environment should quickly restore to its initial state to support the next round of training.

When training scales to thousands of concurrent tasks, the sandbox system needs elastic scheduling capabilities to dynamically allocate computational resources and avoid single-point bottlenecks that drag down overall training efficiency.

3.2 Task Scheduling and Orchestration

Large-scale concurrent tasks require an efficient scheduling system to manage. The scheduling system must address the following:

• Task distribution: Assigning different tasks to appropriate execution nodes.
• Priority management: Dynamically adjusting task priorities based on training phases.
• Timeout and retry handling: Properly managing tasks that time out or encounter exceptions.
• State tracking: Monitoring the execution state of each task in real time to provide a basis for reward computation.

3.3 Reward Signal Design and Computation

Reward signals are the core driving force of reinforcement learning. In complex tasks, reward design typically needs to combine multiple sources:

# Illustrative reward computation logic
def compute_reward(task_result):
    reward = 0.0
    # Basic correctness reward
    if task_result.passed_tests:
        reward += 1.0
    # Code quality reward
    reward += 0.2 * task_result.code_quality_score
    # Execution efficiency reward
    reward += 0.1 * task_result.efficiency_bonus
    # Penalty term
    reward -= 0.3 * task_result.error_count
    return reward

More importantly, reward signals must be sufficiently reliable. If evaluation results are noisy or biased, the model will learn incorrect strategies. Therefore, the quality control of tasks themselves, the rigor of evaluation logic, and the degree of automation in result verification are all critical factors that determine training effectiveness.

3.4 Data Feedback and Iteration Mechanisms

Post-training is not a one-time process but a continuous iterative loop. The training system needs to collect, filter, and annotate the large volume of data generated through model-environment interaction — including successful cases, failed cases, and intermediate states — then feed it back into the next round of training. The efficiency of this closed loop directly determines the speed at which model capabilities improve.

4. Core Variables in Post-Training Competition

From a technical trend perspective, post-training competition is shifting from “algorithm innovation” to “systems engineering capability.” Specifically, the following variables will determine the actual effectiveness of post-training:

First, the quantity and diversity of high-quality environments. How many different task scenarios a model can practice in through trial and error directly determines the breadth of its capability boundaries. Environments need to cover not only multiple task types but also provide sufficient difficulty gradients to support curriculum-style progressive training.

Second, the reliability and density of feedback signals. The more accurate and dense the reward signals, the higher the model’s learning efficiency. This requires task designers to have deep understanding of the tasks themselves and the ability to decompose complex success criteria into quantifiable intermediate metrics.

Third, the stability and efficiency of large-scale systems. When training scales to thousands or even tens of thousands of concurrent tasks, system stability, scheduling efficiency, and resource utilization all become bottlenecks. Delays or failures in any component can significantly slow down overall training progress.

Fourth, the iteration speed of the data closed loop. From interaction data collection to training updates, and then to the next round of interaction validation — the faster this loop runs, the tighter the pace of model capability improvement.

5. Implications for Developers

For engineers working on large model application development, the evolution of post-training technology brings several important insights:

1. Evaluation approaches need to be upgraded. Traditional static benchmark tests are no longer sufficient to comprehensively measure model performance in complex tasks. Developers need to build dynamic evaluation environments that more closely mirror real-world scenarios, focusing on model stability and problem-solving capabilities across multi-turn interactions.

2. Task design becomes a core competency. Whether building training environments or designing reward signals, deep understanding of the task itself is the prerequisite. Engineers who possess domain knowledge and can translate it into executable, verifiable task pipelines will deliver greater value in the post-training era.

3. Infrastructure defines the ceiling. Algorithm-level improvements are certainly important, but without reliable execution environments, efficient scheduling systems, and stable feedback mechanisms as support, even the best algorithms cannot scale into production.

4. Focus on general capabilities rather than specific products. The essence of post-training technology lies in methodology and engineering practice, not in any particular tool or platform. Mastering the fundamental principles of large-scale reinforcement learning and infrastructure design is more valuable in the long run than familiarity with any specific product interface.

6. Conclusion

Post-training is becoming the primary battleground for improving large model capabilities. From static data-driven approaches to dynamic environment interaction, from single-round evaluation to multi-round iteration, this paradigm shift places entirely new demands on training infrastructure. The sandbox systems, scheduling orchestration, reward computation, and data feedback loops that underpin large-scale agent reinforcement learning constitute the core technology stack of this stage. Going forward, those who can achieve breakthroughs in building high-quality environments, designing reliable feedback mechanisms, and engineering large-scale systems will gain the upper hand in post-training competition. For developers and enterprises, understanding the technical logic behind this trend holds greater strategic value than chasing short-term hype.