Large Model Post-Training: The Technical Path from "Answering Questions" to "Getting Things Done"

Over the past two years, the center of gravity in improving large model capabilities has been undergoing a structural shift: industry attention is moving from “how large can we make the model during pre-training” to “how well can we make the model perform during post-training.” Two forces drive this shift. First, the marginal returns from scaling parameter counts and data volume alone are gradually diminishing. Second, complex tasks—especially coding and Agent tasks—now demand capabilities beyond “answering questions correctly,” entering the deeper territory of “getting things done.”

This article does not evaluate any specific model. Instead, it addresses a general technical question: what kind of technical systems and engineering infrastructure does large-scale post-training—especially reinforcement learning for Agent scenarios—actually require?

1. Two Main Lines: The Division of Labor Between Pre-Training and Post-Training

From a training pipeline perspective, large model capability improvement advances along two main lines:

• Pre-training: Determines the model’s “foundation,” including parameter scale, model architecture, and the scale and quality of training data. It endows the model with language understanding, knowledge reserves, and general reasoning potential.
• Post-training: Determines the model’s “usage,” including supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and the increasingly important large-scale reinforcement learning (RL) in recent years.

A phenomenon often discussed in the industry: at the same parameter scale, models produced by different teams can differ significantly on practical tasks, and the main source of this gap is often not pre-training but the depth and quality of post-training. In other words, pre-training determines the ceiling of model capability, while post-training determines how much of that ceiling can be realized.

Of particular note, large-scale reinforcement learning has become a core method for improving model reasoning and complex task handling. Its key value lies in the fact that the model no longer merely imitates human demonstrations but actively explores, trials, and corrects within environmental feedback, thereby achieving a capability ceiling beyond that of demonstration data.

2. Paradigm Shift: From “Answer Correctness” to “Task Success”

To understand why Agent-oriented post-training is difficult, one can first compare two types of tasks.

Math-problem-style tasks: The model outputs a final answer, and the system compares it against a standard answer to determine correctness. The reward signal is sparse but clear, cheap, and reliable.

Complex coding or Agent tasks: The model needs to understand requirements, modify code, run tests, and continue modifying based on error reports, potentially looping through multiple rounds. Judging whether the task was “accomplished” cannot rely on a single-step output but must consider the final result of the entire execution trajectory.

This difference brings three direct consequences:

1. Reward computation shifts from “string comparison” to “environment execution.” The system must actually run code and tests to provide feedback.
2. Training samples shift from “one question, one answer” to “multi-turn trajectories.” A single sample may contain dozens of tool calls and environment interactions.
3. Infrastructure expands from “pure GPU clusters” to a “hybrid system of GPUs plus large-scale CPU execution environments.”

The industry often categorizes such approaches under “verifiable rewards”: in any domain where task results can be objectively verified—whether code passes tests, whether a build succeeds, whether results satisfy constraints—explicit reward signals can be designed to let the model continuously improve through reinforcement learning. This also explains why coding capability is often the “beachhead” of Agent capability: its feedback is naturally executable and verifiable. On this foundation, capabilities can gradually extend to more professional task scenarios that can be practically executed and repeatedly verified.

3. Four Engineering Pillars of Large-Scale Agent RL

If the bottleneck of pre-training is compute and data, then competition in post-training increasingly depends on something harder to replicate: how many high-quality environments the model can repeatedly trial and error in, and obtain reliable feedback. This requires at least four types of infrastructure.

3.1 Sandboxed Execution Environments

Each time the model completes a task, it needs a clean, isolated, disposable runtime. Core requirements for sandboxes include:

• Strong isolation: Any model-generated code should be treated as untrusted code and must be thoroughly isolated from the host environment and other tasks, typically achieved through containers or lightweight virtualization technologies.
• Fast creation and destruction: Large-scale RL may require creating hundreds or thousands of environments per second, and cold-start latency directly determines overall throughput.
• Environment reproducibility: Task environments (dependency versions, initial files, toolchains) must be fixed; otherwise, the same trajectory cannot be reproduced, and rewards become untrustworthy.
• Resource quotas: CPU, memory, disk, network, and execution time must all have upper limits to prevent anomalous tasks from overwhelming the cluster.

3.2 Large-Scale Scheduling Systems

Between trajectory collection for individual tasks and policy updates on GPUs, a high-throughput, fault-tolerant scheduling layer is needed. It must solve several typical problems:

• Heterogeneous resource matching: The policy model performs inference on GPUs, while task execution occurs in CPU sandboxes. The speed difference between the two sides causes resource misalignment, and the scheduling system needs streaming alignment to avoid GPU idling or sandbox backlog.
• Failure handling: Sandbox timeouts, environment crashes, and hung processes are the norm. The scheduling layer must support fast failure, automatic retries, and dirty environment reclamation.
• State consistency: During multi-turn interactions, environment snapshots and model context must correspond one-to-one; any misalignment will corrupt training signals.

An illustrative scheduling loop can be abstracted as follows (pseudocode):

while not rl_job.done():
    batch  = task_sampler.next_batch()          # sample tasks
    envs   = scheduler.acquire_sandboxes(batch) # acquire sandboxes in batch
    trajs  = rollout(policy_model, batch, envs) # collect trajectories via multi-turn interaction
    rewards = [verify(t) for t in trajs]        # compute reward for each trajectory
    rl_job.update(policy_model, trajs, rewards)
    scheduler.release(envs)                     # recycle and destroy sandboxes

This pseudocode glosses over a great deal of engineering detail, but it highlights the key point: trajectory collection, reward computation, and policy updates are three parallel pipelines, and if any one becomes a bottleneck, the throughput of the entire training process collapses.

3.3 Construction and Governance of High-Quality Task Sets

Environments are merely “exam rooms”; the quality of the exam questions themselves also determines training effectiveness. Task governance typically must pass several gates:

• Difficulty stratification: Tasks should cover a distribution from easy to hard. Too easy and the model learns nothing; too hard and it receives zero reward for long periods, leaving gradient signals sparse.
• Diversity and decontamination: Tasks must have sufficient coverage and must be strictly isolated from evaluation sets to prevent data leakage from inflating apparent capability.
• Verifiability: Each task must come with objective acceptance criteria (such as test cases, build scripts, or constraint conditions). Tasks that cannot be verified are better discarded.
• Combination of automatic generation and human validation: Scaled task sets often rely on automatic construction (such as synthesizing change tasks from real code repositories), supplemented by sampled human review to control noise.

3.4 Reliable Reward and Evaluation Systems

If rewards are unreliable, training will drift. Common reward signals in practice, ranked roughly by reliability, are:

1. Unit test pass rate: The hardest signal, but beware of flaky test failures.
2. Build and run results: Whether compilation succeeds, whether runtime errors occur, whether output satisfies assertions.
3. Static checks: Style, type, and security scanning as auxiliary signals.
4. Model-based evaluation: Flexible but noisy, usually used only as a fallback or weighted term.

A problem that must be guarded against is reward hacking: the model may learn to “delete failing test cases” or “make assertions always true.” Therefore, evaluation systems typically require multiple cross-checks, such as freezing acceptance cases, detecting tampering with test files, and introducing adversarial spot checks. A simplified reward function is illustrated below:

def verify(trajectory, task):
    if sandbox_changed_tests(trajectory):    # tampered with acceptance cases, immediate fail
        return -1.0
    if not build(task.repo, trajectory):     # build failed
        return -0.5
    passed = run_frozen_tests(task.repo)     # frozen acceptance tests
    if passed == task.total_tests:
        return 1.0
    return passed / task.total_tests - 0.2   # partial pass gives partial signal

4. Multi-Turn Trajectories: How Training Signals Are Organized

Unlike traditional “one question, one answer” RL, Agent task trajectories are longer and sparser. Engineering must handle three things well:

• Trajectory truncation and step-wise feedback: Overly long trajectories create context and GPU memory pressure, requiring checkpoints at key nodes; process rewards can also be introduced to mitigate the sparsity of “signal only at the endpoint.”
• Online collection rather than offline replay: Large-scale RL typically uses on-policy or near-on-policy methods, meaning trajectory production and model updates must be tightly coupled in real time; data cannot be accumulated in batches.
• Data value of failed trajectories: Categorizing and analyzing failed trajectories is an important source of information for improving task sets and reward design. It deserves a dedicated data pipeline rather than simple discarding.

5. Cost, Stability, and Engineering Trade-offs

The cost structure of large-scale Agent RL differs greatly from pre-training:

• Mixed compute structure: GPU consumption is relatively manageable, but massive CPU execution, sandbox lifecycle management, and storage reads and writes constitute hidden major costs.
• Effective compute rate determines cost-effectiveness: If sandbox preparation takes too long, occasional task failure rates are high, or reward signals are noisy, the effective utilization of GPUs will decline significantly.
• Stability takes priority over peak performance: A single training run may last from days to weeks, and any systematic bias (such as an error in a certain version of the acceptance script) will cause irreversible training contamination. Therefore, full-chain log auditing, monitoring, and alerting are necessities, not options.

For most teams, building this system from scratch has an extremely high barrier. Sandbox security, scheduling throughput, task governance, and reward reliability—each is an independent engineering challenge. A more realistic path is to build on mature infrastructure: enterprises can use general-purpose container platforms on the cloud to host large-scale sandbox scheduling, object storage to manage environment images and trajectory data, and logging services with monitoring and alerting to observe full-chain health. For example, Baidu AI Cloud offers corresponding general product capabilities across these categories that can be combined, allowing teams to focus their efforts on task design, reward modeling, and training strategy—the things that truly constitute differentiation.

6. Conclusion: The Essence of Post-Training Competition

To distill the above into one sentence: competition in pre-training is about compute, data, and architecture, while competition in post-training increasingly depends on how many high-quality environments the model can repeatedly trial and error in, and obtain sufficiently reliable feedback.

This also means that the moat of Agent capability is not mysterious; it is a complex systems engineering endeavor: the safety of sandbox isolation, the throughput of the scheduling system, the quality of task sets, and the trustworthiness of reward signals—all four are interlinked, and any weak link directly caps the model’s capability ceiling. For engineering teams, understanding this system has more long-term value than tracking any single model version number, because versions will iterate, but the methodology of “repeatedly trial and error in high-quality environments and realize reliable feedback” will remain effective for a long time to come.