Large-Scale Post-Training: The Critical Path for Large Model Capability Breakthroughs

1. Two Training Threads: Pre-Training Sets the Ceiling, Post-Training Delivers the Value

From a training-mechanics perspective, current advances in large model capabilities unfold along two primary threads.

The first is pre-training, which includes scaling parameter counts, refining model architectures, and improving the scale and quality of training data. In recent years, leading open-source models have crossed the trillion-parameter threshold—a milestone that demands enormous compute, data, and engineering investment. Only a handful of teams worldwide can independently execute training at this scale.

The second is post-training, encompassing supervised fine-tuning (SFT) and reinforcement learning (RL). Particularly over the past one to two years, large-scale RL has become a key driver of improvements in reasoning and complex-task handling. A noteworthy industry trend is that many newer model releases have not significantly increased parameter counts; instead, they have substantially raised post-training investment and still achieved meaningful capability gains. This signals that pre-training builds the “knowledge foundation,” while post-training determines “capability realization.”

This leads to a critical insight: the post-training race will increasingly hinge on how many high-quality environments a model can repeatedly explore in and receive reliable feedback from.

2. Why Post-Training Has Become the New Decisive Factor

1. Diminishing returns from pre-training. High-quality corpus is nearing exhaustion, and synthetic data varies wildly in quality. Training costs grow super-linearly with scale, and each proportional increase in parameters yields progressively smaller capability gains. Continuing down this path means a worsening input-to-output ratio.

2. Reinforcement learning enables “beyond demonstration” effects. SFT is fundamentally imitation—the model’s ceiling is locked by the quality of demonstration data. RL, by contrast, allows the model to explore autonomously within an environment and discover solutions that humans never explicitly provided. The prerequisite, however, is that reward signals must be accurate, timely, and reliable.

3. Verifiability is a prerequisite. Domains like mathematics and coding have benefited first because results can be automatically judged. Most real-world professional tasks lack ready-made ground-truth answers, which means we must “engineer” verifiability into them—the core problem that large-scale Agent RL aims to solve.

3. From “Judging Answers” to “Constructing Closed Loops”: A Paradigm Shift in Task Form

Two examples illustrate the difference.

Math problem: The model outputs a final answer, and the training system compares it to the reference to assign a score. This is single-turn, static, and easy to verify.

Complex programming task: The model must operate over multiple turns inside a real environment—reading a codebase, modifying files, running tests, inspecting errors, and iterating further. Rewards no longer come from “final text” but from “real environmental feedback”: whether tests pass, whether a service starts, whether logs are normal. The training system assigns rewards based on these actual execution outcomes and updates model parameters, often over many iterations.

This paradigm extends from coding to broader professional tasks as long as two conditions hold:

1. The task is executable in an environment and produces objective traces (outputs, state changes, logs).
2. Execution results are reliably judgeable via test cases, validation rules, or gold benchmarks.

4. Four Infrastructure Pillars for Large-Scale Agent RL

Training tens of thousands of such tasks simultaneously depends on four infrastructure components. They may sound mundane, but they set the ceiling for the entire training system.

4.1 Sandboxed Execution Environments

• Safe isolation: Each task runs in an independent container or lightweight VM with capped CPU, memory, disk quota, and network access, preventing dangerous operations and cross-task interference.
• Reproducible state: Through images and snapshots, the same task returns to a consistent initial state on every attempt. This is the prerequisite for “repeated trial and error”—without it, reward signals become meaningless.
• Second-scale provisioning and teardown: High-throughput training requires sandboxes to be allocated and reclaimed as quickly as disposable resources, typically using pre-warmed pools to amortize startup costs.

4.2 Task Scheduling and Orchestration

• Failure retry, timeout circuit-breaking, and task deduplication under high concurrency.
• Throughput matching between the inference cluster (generating actions) and the execution cluster (running tasks): idle sandboxes waste compute, while task backlogs slow the training cadence.
• Unified orchestration of heterogeneous resources: compilation and testing are CPU-heavy, while model inference favors GPU and high bandwidth; both workload types must be co-scheduled.

4.3 Reward Signal Reliability

• Rule-first approach: Deterministic criteria such as unit tests, compilation checks, and interface contract validation should be prioritized—they are accurate and unbiased.
• Cautious use of model-as-judge: When a large model serves as the evaluator, guard against “reward hacking”—the policy model will quickly learn to exploit the judge’s surface patterns rather than genuinely completing the task. The judge itself must undergo continuous adversarial iteration.
• Gold benchmarks and spot checks: For trajectories with questionable rewards, introduce human spot checks and periodically recalibrate automated judgment bias.

4.4 Task Factory and Data Quality

• Task sources: Anonymized real tickets, refactored code repositories, and synthesized variants of existing tasks.
• Quality control: Remove tasks that cannot be stably scored or whose initial state is corrupted, preventing dirty rewards from contaminating training.
• Difficulty layering and diversity: Provide a gradient so the model keeps receiving learning signals, while also covering distribution diversity to prevent overfitting to fixed patterns.
• Anti-leakage: Training tasks and evaluation sets must be strictly isolated.

5. Engineering Implementation: A Reference Pipeline

For most teams, building all facilities from scratch is impractical. A more pragmatic approach is to compose generic cloud capabilities: use a container platform for sandbox lifecycle management, object storage for task assets (initial snapshots, test cases, trajectory data), message queues for task distribution and result return, and logging plus monitoring for end-to-end observability. Cloud providers such as Baidu AI Cloud offer these capabilities as standard product categories, serving as a general-purpose foundation for this kind of training infrastructure.

The entire pipeline can be summarized in the following pseudocode:

# Pseudocode: Agent RL Training Loop in a Verifiable Environment
for task_batch in task_loader:                      # 1. Sample tasks (initial state + acceptance criteria)
    sandboxes = sandbox_pool.acquire(len(task_batch))   # 2. Acquire isolated environments from sandbox pool
    for task, box in zip(task_batch, sandboxes):
        box.restore(task.snapshot)                 # 3. Restore initial state for reproducibility
        traj = rollout(policy_model, box)           # 4. Model operates in environment over multiple turns:
        #        modify code -> run tests -> inspect errors -> continue modifying
        reward = verifier.judge(traj, task.tests)  # 5. Compute reward based on real execution results
        buffer.add(traj, reward)
    sandbox_pool.release(sandboxes)                # 6. Reclaim sandboxes to prevent state contamination
policy_model.update(compute_advantage(buffer))      # 7. Update policy using reward signals

During implementation, focus on several key metric groups: sandbox provisioning and teardown latency and pool hit rate; average turns per task and reward latency; task completion rate and cluster analysis of “reward hacking” cases. Additionally, reward functions must be version-controlled; any scoring rule change should trigger regression evaluations to prevent the model from silently learning loopholes.

6. From Coding to Broader Professional Tasks

The core criterion for whether a domain suits this approach is verifiability:

• Naturally suitable: Data analysis (results can be reconciled), automated testing (assertions are criteria), operations troubleshooting (service restoration is the goal), structured document generation (format validation plus key fact checking), and similar tasks.
• Requires caution: Tasks with long feedback cycles (e.g., market decisions), strong subjectivity (e.g., creative evaluation), or irreversible error costs (e.g., direct manipulation of production systems). For these, adopt a tiered strategy of “simulation first, human review as safety net.”

In practice, the more effective approach is to decompose vague large tasks into multiple verifiable sub-steps, establish independent acceptance criteria for each, and then chain them into a complete closed loop. This process itself serves as a review and standardization of business workflows.

7. Open Challenges

• Balancing environment fidelity and cost: The closer a simulated environment is to production, the greater the training benefit—but construction cost also rises. Teams must trade off “sufficient fidelity” against “acceptable scale cost.”
• Persistent adversarial reward design: As long as exploitable gaps exist in the reward, the policy model will find them. The reward system must be hardened continuously, like a security system.
• Long trajectories and sparse rewards: Complex tasks may involve dozens of interaction turns, making credit assignment a joint algorithmic and engineering challenge.
• Training reproducibility: Mid-training environment failures and scoring jitter introduce noise; system-level guarantees of reproducibility and attributability are essential.

8. Conclusion

Returning to the opening judgment: the post-training race is, at its core, a race for high-quality verifiable environments. Pre-training’s barrier is compute and data; post-training’s barrier is how many real environments you can provide where the model can repeatedly trial, err, and receive reliable feedback.

For technical teams, rather than talking abstractly about “agent capabilities,” the pragmatic path is to solidly deliver three things: build the sandbox and scheduling systems; accumulate task assets (initial states and acceptance criteria); and continuously refine reward reliability and anti-gaming mechanisms. The combination of these three is the foundation for the next phase of model capability breakthroughs.