From Pre-Training to Post-Training: The Dual Engines of LLM Capability

In recent years, the path to improving large language model capabilities has become increasingly clear, and it can be summarized in two core directions. The first is pre-training, which pushes the ceiling of foundational capabilities by scaling model parameters, optimizing underlying network architectures, and improving the quantity and quality of training data. The second is post-training, which encompasses strategies such as supervised fine-tuning (SFT) and reinforcement learning (RL). As foundation models gradually converge, the marginal returns from pre-training have begun to diminish. Post-training centered on large-scale reinforcement learning is now becoming the decisive factor for improving a model’s complex reasoning and real-world task execution capabilities.

Recently, a leading team in the industry, when releasing a new generation of an ultra-large-parameter model, did not blindly pursue the expansion of pre-training scale. Instead, it shifted its strategic focus substantially toward post-training. This move sends a clear technical signal: through large-scale agent reinforcement learning, the execution and error-correction capabilities already validated in the programming domain can be generalized and transferred to a broader range of professional task scenarios. The competitive moat in post-training has evolved into how many high-quality real environments a model can repeatedly learn from through trial and error—and how reliably it can obtain feedback signals from them.

The Evolution of Task Verification Paradigms: From Static Comparison to Dynamic Execution

To understand the technical core of this shift, we need to examine the evolution of task verification mechanisms.

In early model training, tasks were often static with well-defined outcomes. Take mathematical calculation as an example. The system only needed to compare the model’s final answer against the reference answer—either as a string or a numeric value—to easily determine correctness and assign a reward. However, when the task shifts from “provide an answer” to “complete a complex engineering programming task,” static comparison breaks down entirely. Complex engineering tasks require the model not only to generate code but also to understand context, handle dependency conflicts, and deal with boundary exceptions.

At this point, the model must operate within a real execution environment: modifying code, compiling and building, running test cases, capturing exception stack traces, analyzing failure causes, and iterating corrections accordingly. This requires the training system to upgrade from simple “question-answer evaluation” to complex “process verification.”

Building Large-Scale Agent RL Infrastructure: Core Architecture

To enable tens of thousands of agents to simultaneously engage in multi-turn trial and error within real environments, an extremely large and highly reliable underlying infrastructure must be built. This is not merely a matter of stacking compute; it is an extreme test of system architecture design. The core modules mainly include the following:

1. High-Concurrency Sandboxed Execution Environment

The sandbox is the isolation boundary between the model and the physical world. In reinforcement learning, every piece of code generated by the model needs to be safely compiled and executed. Large-scale parallel training means the system needs to dynamically create tens of thousands of containerized sandboxes simultaneously. These sandboxes must have fast cold-start capabilities, strict resource quota limits (to prevent malicious code from exhausting host resources), and network isolation mechanisms. The industry typically uses lightweight virtualization technologies or container orchestration platforms to achieve this, ensuring that each task completes its lifecycle in an independent space without interference.

2. Task Orchestration and Scheduling System

Because complex tasks require multi-turn interaction, the scheduling system needs to maintain complex task state machines. A task may involve a loop of “generate—execute—error—regenerate.” The scheduler is responsible not only for allocating compute resources but also for managing task queue priorities, handling timed-out tasks, and reclaiming idle sandboxes. When facing tens of thousands of concurrent tasks, the scheduling system itself can easily become a bottleneck. Therefore, distributed message queues are typically introduced to achieve asynchronous decoupling and high-availability flow of task states.

Below is a simplified pseudocode example showing how the scheduling system manages the interaction loop with sandboxes:

def run_agent_rl_episode(task_prompt, max_steps=5):
    # Initialize an isolated sandbox environment
    sandbox = SandboxCluster.acquire_isolated_env()
    context = {"prompt": task_prompt, "history": []}
    for step in range(max_steps):
        # Model generates an action (e.g., modifying code in a specific file)
        action = LLM.predict(context)
        # Execute the action in the sandbox
        exec_result = sandbox.execute(action)
        # Evaluate the execution result
        if exec_result.success:
            reward = calculate_reward(exec_result)
            break
        else:
            # Extract error information and feed it back to the model
            context["history"].append(exec_result.stderr)
            reward = penalty_score()
    SandboxCluster.release(sandbox.id)
    return reward

3. Task Quality and Reliable Feedback Mechanisms

The effectiveness of reinforcement learning depends heavily on the accuracy of reward signals. If the feedback signal itself contains noise or errors, the model will not only fail to learn an effective policy but may even exhibit “reward hacking”—that is, the model learns to exploit loopholes in the feedback mechanism to obtain high scores rather than genuinely solving the problem.

Therefore, ensuring the reliability of the feedback mechanism is the top priority in post-training. This requires:

• High-quality task design: Task instructions must be clear and unambiguous, and test cases must cover both normal paths and various boundary conditions.
• Fault-tolerant evaluation logic: The system needs a certain degree of fault tolerance, such as filtering out non-code-logic errors caused by environment instability.
• Multi-dimensional signal fusion: Beyond simple pass/fail signals, additional metrics such as code coverage, execution efficiency, and resource consumption can be introduced to build a continuous reward function.

Generalization of Code Capabilities: From Single Execution to General Intelligence

Based on the infrastructure described above, the real breakthrough brought by large-scale agent RL lies in capability generalization. Programming tasks are well-suited as a training entry point because they come with highly mature verification toolchains (such as compilers and testing frameworks). Once the model learns the closed-loop logic of “understand the goal—formulate a plan—execute actions—observe feedback—adjust strategy” in this highly deterministic environment, this underlying thinking capability can be transferred to other professional tasks that lack clear verification standards.

For example, in data processing, automated operations and maintenance, and even some business decision-making scenarios, although there is no absolutely objective evaluation tool like a compiler, domain-specific evaluators or model-based judging mechanisms can be constructed to reuse the sandbox and scheduling architecture established during code training.

Infrastructure Is the Future: A Summary and Outlook from an Engineering Perspective

In conclusion, competition in the post-training phase of large models has long moved beyond the pure algorithm level and evolved into a comprehensive contest of underlying systems engineering. Whoever can build more stable, higher-concurrency, and more reliable agent training infrastructure will gain an advantage in complex real-world application tasks.

For enterprises and developers, building such systems means deeply integrating container platforms, distributed storage, message queues, and comprehensive monitoring and alerting systems. In the future, as reinforcement learning penetrates more vertical domains, we may even see the emergence of standardized “Agent Training Environment as a Service” architectures. At that point, models will truly evolve from being “articulate” to becoming autonomous agents that “can do things and get things done.”