Large-Scale Post-Training: The Critical Path to Advancing LLM Capabilities

1. Two Main Paths for Improving LLM Capabilities

From a training pipeline perspective, current efforts to improve large language models (LLMs) generally follow two paths:

1. Pre-training: expanding parameter scale, improving model architecture, and increasing the quantity and quality of training data to strengthen the model’s general knowledge foundation. In recent years, open-source model sizes have climbed steadily from tens of billions to trillions of parameters. The compute, data, and engineering systems required for training at this scale remain accessible to only a very small number of teams globally.

2. Post-training: building on a base model through supervised fine-tuning (SFT) and reinforcement learning (RL) to align it with task objectives and unlock reasoning potential. Over the past one to two years in particular, large-scale reinforcement learning has become a key method for improving reasoning and complex task performance.

A notable industry signal is that once parameter scale reaches a certain level, the marginal benefit of simply scaling pre-training begins to decline. Leading teams are clearly shifting more investment toward post-training—using the same base model but applying more thorough and refined post-training to unlock stronger task-completion capabilities.

2. Why Programming Is the Testing Ground for Post-Training

To understand what post-training does, it helps to first understand where reward signals come from.

Math problems are the simplest case: the model produces an answer, and it can be checked against a reference answer. This kind of reward signal is objective, inexpensive, and easy to produce at scale in parallel, making it ideal material for early reinforcement learning.

But real-world professional tasks are not that simple. Take a complex programming task as an example. The full execution loop looks like this:

• The model understands the requirement and writes code.
• It runs tests in a real environment and observes error messages.
• Based on the errors, it locates the problem, modifies the code, and runs it again.
• This process may repeat for multiple rounds until all tests pass.

This means the reward is no longer about “matching an answer” but about “seeing the result.” Only when the model receives real execution results can the training system produce reliable rewards and update parameters accordingly. Programming has become the preferred scenario for large-scale reinforcement learning because it naturally satisfies three conditions: verifiable outcomes (test cases), executable processes (real execution), and automatable feedback (automated judgment similar to continuous integration).

3. Three Infrastructure Pillars for Large-Scale Agent RL

Once the training objective shifts from “answering questions” to “completing tasks,” the training system itself becomes a complex distributed engineering problem. At a high level, three infrastructure components are essential.

3.1 Large-Scale Sandbox Execution Environment

Every action the model takes—modifying code or running commands—requires a safe, isolated runtime environment that can be created and destroyed quickly. When thousands of tasks run concurrently, the startup speed, throughput, and isolation strength of the sandbox pool directly determine training efficiency. Image preheating, dependency preinstallation, CPU and memory quota control, and network isolation all require careful engineering.

3.2 Task Scheduling and Orchestration

Each individual task involves a multi-round loop, while overall training involves massive concurrency. Together, these place high demands on the scheduling system: queue management, failure retries, timeout circuit breaking, and result collection. Problems in any part of this chain can slow down the entire process. In practice, such systems are typically built from common cloud capabilities such as container platforms, message queues, object storage, and logging services. For example, on platforms like Baidu AI Cloud, a common architecture uses a container platform to host sandboxes, a message queue to decouple task distribution, and a logging service to replay execution traces.

3.3 Task Quality and Reward Reliability

The biggest risk in large-scale training is not a shortage of tasks but unreliable rewards. If test cases themselves contain flaws, the model may learn to game the system rather than actually solve the problem—a phenomenon known as reward hacking. Therefore, task set construction requires deduplication, difficulty stratification, and prevention of data contamination. The evaluation system should combine unit tests, rule-based checks, and model-assisted review, with sampled manual verification of tasks marked as “passed.”

The overall training loop can be illustrated with the following pseudocode:

# Large-scale agent reinforcement learning training loop (illustrative)
for batch in task_queue.pull(batch_size=4096):
    sandboxes = sandbox_pool.acquire(len(batch))   # Acquire isolated sandboxes in bulk
    trajectories = []
    for task, box in zip(batch, sandboxes):
        for step in range(task.max_steps):
            action = policy(task.observation)      # Model decision
            result = box.execute(action)           # Real execution: modify code / run tests
            if result.all_tests_passed:
                break
        trajectories.append(rollout(task, result)) # Generate reward from verifiable result
    sandbox_pool.release(sandboxes)                # Quickly recycle sandboxes for reuse
    trainer.update(policy, trajectories)           # Update policy parameters

Once this loop is actually running, the bottleneck is often not the algorithm but the engineering: sandbox startup must be fast, execution results must be trustworthy, and failed tasks must be automatically recycled and requeued.

4. From Programming Skills to Broader Professional Tasks

A clear industry trend is that teams are attempting to use large-scale post-training to extend the capabilities accumulated in programming—reading and writing code, executing verification, and iterative repair—to more professional tasks that can be practically executed and repeatedly verified. Typical candidate directions include:

• Data analysis: generating SQL or analysis scripts, running them in a data environment, and verifying the correctness of results.
• Automated operations: generating operational scripts, rehearsing them in a simulation environment, and then determining whether they are safe to deploy.
• Document and process processing: generating structured output according to rules and using validators to automatically determine compliance.
• Scientific computing: reproducing numerical experiments and automatically comparing whether result errors fall within thresholds.

To judge whether a given type of task is suitable for large-scale agent RL, three criteria can be applied: verifiable outcomes, reproducible environments, and automatable feedback. The more tasks that satisfy these three points, the more capabilities models can be taught.

5. Engineering Practice Recommendations

For teams preparing to build a large-scale post-training system, here are five practical recommendations:

1. Prioritize environment consistency: use image snapshots and dependency locking to ensure every sandbox starts from the same initial state, avoiding reward signal contamination from environment drift.
2. Full-path replayability: use logging services to completely record every action and execution output so that when training anomalies occur, you can distinguish whether the model was wrong or the environment was wrong.
3. Cost-elastic design: sandbox load fluctuates sharply with the training rhythm, so auto-scaling strategies should be adopted to balance throughput and cost.
4. Minimize the security boundary: sandboxes should deny network access by default or allow only whitelisted access, with filesystem and permissions configured according to the principle of least privilege.
5. Data closed loop: turn failure cases exposed during training into new tasks and new test cases, allowing the task set to evolve together with model capabilities.

6. Conclusion

The competition in post-training may appear to be about algorithms and compute, but it is essentially evolving into a competition over high-quality verifiable environments. How many reliable environments a model can use for repeated trial and error—and how much trustworthy feedback it can receive—determines the ceiling of its capabilities. Pre-training determines what a model knows; post-training determines what a model can accomplish. For engineering teams, the earlier they solidify the three infrastructure pillars of sandboxes, scheduling, and evaluation, the better positioned they will be in the next stage of model capability competition.