Post-Training for Large Models and Engineering Practices in Agent Reinforcement Learning

Two Main Paths for Capability Improvement

The continued evolution of large model capabilities can broadly be grouped into two technical routes.

The first is the pre-training side, which includes scaling parameter counts, improving model architecture, and increasing the scale and quality of training data. The marginal returns on this route are becoming more complex: increasing parameters still yields capability gains, but it places higher demands on data quality, training stability, and compute orchestration. The number of organizations capable of independently conducting ultra-large-scale pre-training is limited globally.

The second is the post-training side, including supervised fine-tuning (SFT) and reinforcement learning (RL). Especially in the past year or two, large-scale RL has become a primary method for enhancing model reasoning and complex task-handling capabilities. Compared to pre-training, post-training is closer to “targeted capability development”—it does not pursue broad general knowledge but focuses on intensive refinement across specific capability dimensions.

A notable trend is that competition in post-training is shifting from the algorithmic level to the engineering level. In other words, the upper bound of post-training effectiveness is no longer determined solely by how sophisticated the reward function design is, but by how many high-quality environments the model can iterate through, and how reliably it can receive feedback.

From “Right or Wrong” to “Completing the Task”

In the past, training models for math problems involved relatively simple feedback signals: the model produced an answer, compared it against a standard answer, and correctness was immediately determined. The verification cost for these tasks is extremely low, making them suitable for large-scale parallel sampling.

However, when the goal becomes “teaching the model to complete a complex coding task,” the nature of the problem changes. Task correctness is no longer decided by a single answer, but by an entire set of execution results:

• Whether the code compiles;
• Whether unit tests all pass;
• Whether edge cases are handled correctly;
• Whether performance meets constraints;
• Whether regressions are introduced.

This means the training system must provide the model with a real execution environment, allowing it to modify code, run tests, observe errors, and continue iterating. The training system then computes reward signals based on actual execution results and updates model parameters. This process often requires multiple rounds of iteration, rather than a single sampling pass.

The following pseudocode describes this closed loop:

for episode in range(max_episodes):
    # 1. Sample an executable task from the task pool
    task = task_pool.sample()
    # 2. Initialize the environment in an isolated sandbox
    env = sandbox.create(task.repo, task.runtime)
    # 3. Multi-turn interaction between model and environment
    for step in range(max_steps):
        action = policy_model(env.observation())
        result = env.execute(action)   # Modify code / run tests
        if result.done:
            break
    # 4. Compute reward based on execution results
    reward = compute_reward(result.tests, result.performance)
    # 5. Update the policy
    policy_model.update(reward)

This logic looks simple on the surface, but supporting it in engineering requires solving a series of infrastructure problems.

Key Infrastructure for Large-Scale Agent RL

When training scales from dozens of tasks to tens of thousands of concurrent tasks, the following capabilities become necessary.

3.1 Large-Scale Sandbox and Scheduling Systems

Each task requires an isolated execution environment, including the code repository, dependencies, runtime, and test framework. These environments must provide:

• Strong isolation: Tasks must not interfere with one another, covering file systems, networks, processes, and resource quotas;
• Fast startup: Cold-start time directly affects training throughput. Image pre-warming and snapshot recovery are common optimization methods;
• Orchestration: The scheduling system must dynamically allocate compute resources based on task priority, resource requirements, and failure-retry policies;
• Observability: Execution logs, resource usage, and exit status from each sandbox must be fully collected for reward computation and troubleshooting.

A common architecture is a three-layer structure: “scheduling layer + sandbox pool + image repository.” The scheduling layer handles task distribution and lifecycle management, the sandbox pool handles rapid environment creation and recycling, and the image repository handles versioning and distribution of runtime environments.

3.2 Reliability of Execution Feedback

The reliability of reward signals directly determines whether training converges. If test cases themselves are flawed, or if the execution environment is unstable, the model will learn incorrect strategies. Therefore, the following measures are needed:

• Test governance: Validate the quality of test cases bundled with tasks; remove flaky tests and those with insufficient coverage;
• Execution determinism: Eliminate randomness wherever possible, such as fixing random seeds and isolating external dependencies;
• Multi-run verification: Execute the same task multiple times to check result consistency;
• Anomaly detection: Distinguish environment errors from model errors to avoid mistaking infrastructure failures for model failures.

3.3 Task Quality and Diversity

The effectiveness of RL is highly dependent on task distribution. If tasks are too homogeneous, the model tends to overfit to specific patterns; if tasks are too difficult, rewards become sparse and training stalls. In practice, the following is typically required:

• Establish a task difficulty grading system;
• Maintain diversity across task types (different languages, frameworks, and problem domains);
• Dynamically adjust sampling weights so the model receives the most training signal within the “challenging but achievable” difficulty range.

From Coding Ability to General Verifiable Tasks

The uniqueness of the coding domain lies in its inherent executability and verifiability—passing or failing tests is an objective signal. This makes coding an ideal testing ground for large-scale RL training.

But the more valuable direction is extending this “executable, repeatedly verifiable” model to more professional tasks. Examples include:

• Data processing pipelines: Can the transformation logic generated by the model run on real data and produce results that match expectations?
• Configuration and deployment tasks: Can the configuration provided by the model bring a service up and pass health checks?
• Query and retrieval tasks: Can the queries constructed by the model return the correct result sets?
• Process automation tasks: Can the multi-step operations orchestrated by the model complete end-to-end without side effects?

The common characteristic of these tasks is that correctness is determined by execution results, not by subjective human evaluation. Once a task possesses this characteristic, it can be incorporated into the Agent RL training loop.

Engineering Observations from Practice

Based on the above analysis, several engineering-oriented observations can be made.

First, the bottleneck in post-training is shifting from algorithms to infrastructure. Reward function design is certainly important, but without a sufficiently large, stable, and diverse execution environment, even the best algorithms cannot reach their potential. Sandbox scheduling, image management, and log collection—these “dirty work” tasks—actually determine the upper bound of training efficiency.

Second, the quality of feedback signals matters more than quantity. A large volume of low-quality, noisy feedback can be worse than a small amount of high-quality feedback. Rather than blindly expanding the task pool, it is more effective to first establish a task quality governance mechanism.

Third, task distribution requires ongoing operation. The task pool is not built once; it needs continuous maintenance, updates, and retirement of obsolete tasks. As model capabilities improve, previously difficult tasks become easy, and new challenging tasks must be continuously introduced.

Fourth, verifiability is the boundary for expansion. Whether a task can be included in the training loop depends on whether it can be automatically verified. This means that any professional scenario with a reliable verification mechanism could become a focal point for capability growth in the next phase.

Summary

The improvement of large model capabilities is shifting from “scaling parameters and data” to “scaling environments and feedback.” Pre-training establishes the model’s base capabilities, while post-training—especially post-training based on large-scale Agent RL—determines whether those capabilities can be deliberately transferred to real-world tasks.

For developers, understanding this technical route matters because it reveals the engineering essence of model capability growth. What is truly scarce is not algorithmic inspiration, but the infrastructure capable of supporting large-scale trial-and-error, along with high-quality task systems that provide reliable feedback. Whoever executes more solidly on these two fronts is more likely to take the lead in the next phase of capability competition.