Deep Dive: Core Evolution and Practical Paths in the Post-Training Era of Large Models

Deep Dive: Core Evolution and Practical Paths in the Post-Training Era of Large Models

In recent years, as the parameter scale of large language models (LLMs) approaches the physical and computational limits, the industry’s approach to enhancing model capabilities has shifted from mere “scale expansion” to more refined “capability elicitation.” Recently, after an open-source model released a version approaching 3T parameters, its subsequent iterations significantly increased the investment in post-training. This technical trend clearly points to an industry consensus: pre-training determines the lower bound of an LLM’s knowledge, while post-training determines the upper bound of its ability to solve complex, professional tasks. Specifically, using large-scale reinforcement learning (RL) to generalize capabilities from strong-logic domains like programming to more executable and verifiable professional scenarios has become the core thread of current technological evolution.

I. Dual-Track Evolution: The Interplay Between Pre-training and Post-training

From an engineering perspective, breakthroughs in current LLM capabilities generally rely on two directions. They are complementary but have distinctly different focuses at various stages of development.

1. Pre-training Stage: Building the Foundation and Knowledge Injection
The core goal of the pre-training stage is to construct the model’s world knowledge base. Primary methods include expanding model parameter scales (e.g., moving toward trillions of parameters), optimizing underlying architectures (e.g., improving attention mechanisms to support longer contexts), and increasing the scale and cleaning quality of training data. However, pre-training faces significant diminishing marginal returns. When the parameter scale reaches a certain threshold and high-quality corpora gradually deplete, simply relying on increased computing power and data struggles to yield linear growth in capabilities. Furthermore, training costs and cluster fault tolerance become extremely high barriers.

2. Post-training Stage: Behavior Alignment and Capability Elicitation
Post-training is the process of “fine-tuning” the base model, primarily involving Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). In the post-training stage, especially over the past year or two, large-scale reinforcement learning has become a key means for improving model reasoning and complex task-processing capabilities. If pre-training is about enabling the model to “store knowledge,” post-training is about teaching it “how to think and act.” Through post-training, models not only align more precisely with human intent but also demonstrate astonishing problem-solving abilities in fields requiring rigorous logic, such as mathematical derivation and code generation.

II. Core Driver of Post-training: Large-Scale RL and Verifiable Feedback

Why has large-scale reinforcement learning come to dominate post-training? The fundamental reason lies in the introduction of verifiable feedback.

In traditional SFT, models learn by imitating human-annotated data. However, human annotations are often subjective and struggle to cover all edge cases. In RL, models update their policies by exploring the environment and receiving reward signals. For tasks like programming and mathematics, feedback is objective and verifiable: Does the code compile? Do unit tests execute successfully? Is the result of the mathematical derivation accurate?

This environment-based verification mechanism allows models to self-evolve through repeated trial and error. The focus of model training is shifting from “relying on the absolute quality of human annotations” to “relying on the frequency of high-quality environment trial and error.” This means the competitive barrier in post-training has evolved: whoever can build more high-quality, automatically evaluable trial-and-error environments will train models with stronger complex task-processing capabilities.

III. Generalization from Coding to Professional Tasks: Building an Executable Closed Loop

Recent technological iterations within the industry indicate that using post-training to expand a model’s strong logic capabilities—accumulated in the programming domain—into more executable and repeatedly verifiable professional tasks is a proven path to success. Why coding? Because code is the most structured and logically rigorous language, possessing natural conditions for sandbox execution and verification.

Generalizing this capability to other professional tasks requires building a complete “perception-decision-execution-feedback” closed loop. In engineering practice, this typically manifests as equipping the model with tool-calling capabilities. Below is a generic pseudo-code example of a tool-calling feedback closed loop, demonstrating how a model iterates based on environmental feedback:

def reinforcement_training_loop(model, task_env, max_steps=100):
    """
    Reinforcement learning post-training closed loop example
    :param model: The large language model to be trained
    :param task_env: Executable, verifiable task sandbox environment
    """
    for episode in range(max_steps):
        # 1. Model receives task state
        state = task_env.get_initial_state()
        # 2. Model generates an action (e.g., generating code, calling an API, executing a command)
        action = model.generate_action(state)
        # 3. Environment executes the action and provides objective feedback
        execution_result = task_env.execute(action)
        # 4. Verification mechanism calculates the reward (e.g., unit test pass rate, business logic validation)
        reward = task_env.verify(execution_result)
        # 5. Model updates its policy based on the reward signal (e.g., PPO/DPO algorithms)
        model.update_policy(state, action, reward)
        # 6. Environment state transition
        state = task_env.get_next_state(execution_result)
    return model

Through this mechanism, models can not only write good code but also learn how to operate databases, call message queues to publish events, and troubleshoot system anomalies via logging services. As long as the task’s goal can be quantitatively verified, the model can master that professional capability during the post-training process.

IV. Engineering Practices and Cloud-Native Support for Enterprise Post-training

For enterprise developers, understanding the principles of post-training is only the first step; the real challenge lies in implementation. Constructing a high-quality trial-and-error environment requires not only algorithmic capabilities but also robust underlying infrastructure support. When building an enterprise-level post-training pipeline, multiple cloud-native capabilities usually need to be integrated:

1. Elastic Computing and Container Orchestration
Large-scale reinforcement learning requires massive concurrent trial and error. For example, a model in training might need to simultaneously launch thousands of sandbox environments to verify generated code or scripts. This demands that the underlying container platform possesses extreme elastic scaling capabilities, able to spin up hundreds or thousands of compute instances in seconds and rapidly release resources upon task completion to control training costs.

2. Data Flow and Persistent Storage
During the trial-and-error process, the model generates massive amounts of trajectory data (including states, actions, rewards, etc.). This data needs to be efficiently collected, cleaned, and stored for policy update computations. High-throughput message queues can be used to asynchronously collect execution feedback, while object storage is suitable for persisting massive historical trial-and-error records, providing support for subsequent data analysis and model review.

3. Observability and Monitoring Alerts
Post-training is a process with strong black-box attributes, and models may exhibit “reward hacking”—finding a shortcut to obtain high rewards without actually solving the real problem. Therefore, a comprehensive monitoring and alerting system must be established. By performing real-time collection and visualization of training metrics (such as Loss, average Reward, KL divergence, etc.) as well as resource consumption metrics of the sandbox environments, development teams can promptly detect training anomalies and intervene.

V. Conclusion and Outlook

The development of large models has entered the “deep water zone.” Constructing a knowledge base through pre-training with parameters approaching or exceeding a trillion, followed by capability elicitation through large-scale reinforcement learning post-training, has become the standard paradigm for industry-leading technologies. The expansion from coding to more executable and verifiable professional tasks is essentially a deep participation in the digitalization of the real world.

For developers and enterprises, the future competitive focus will no longer be limited to model parameter size, but rather on who can build richer, more rigorous, and higher-quality trial-and-error and feedback environments. In this process, combining mature cloud computing infrastructure—such as container orchestration, object storage, and intelligent monitoring—will be the key to ensuring the efficient and stable implementation of post-training engineering. Models are not just “learning knowledge” but “verifying truth through practice,” which is the necessary path for artificial intelligence to advance toward general reasoning capabilities.