Beyond Coding: Scaling LLM Capabilities for Professional Tasks via Post-Training

I. Two Main Tracks for Enhancing LLM Capabilities

From a training pipeline perspective, the capability development of current large language models (LLMs) generally falls into two phases.

The first phase is pre-training. Methods include scaling parameter sizes, optimizing model architectures, and increasing the volume and quality of training data. The industry has recently seen open-source models approaching the 3T parameter mark, but globally, the number of teams capable of funding and executing training at this scale can be counted on one hand. A single training run requires a cluster of thousands of GPUs to operate continuously for weeks, involving complex system engineering challenges such as data cleaning pipelines, parallel strategy design, fault tolerance, and checkpoint recovery. The cost of trial and error is exceptionally high.

The second phase is post-training. This includes Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and the large-scale reinforcement learning (RL) that has rapidly emerged over the past year or two. Contrary to the traditional perception that post-training is merely “alignment polishing,” it has now become a critical variable determining the upper limit of a model’s reasoning capabilities. In particular, reinforcement learning based on verifiable rewards is becoming the core method for enhancing model reasoning and complex task processing. A notable industry trend is that leading teams are significantly increasing their post-training investments on top of existing strong coding baselines, aiming to transfer the capabilities models have accumulated in programming to a wider range of executable and verifiable professional tasks.

II. The Diminishing Returns of Pre-Training Scale

Pre-training remains the foundation, but the marginal cost-to-benefit curve is becoming steeper:

1. Diminishing Returns: The loss reduction brought by parameter scaling follows a power law. Doubling model size yields increasingly limited practical capability gains, while training and inference costs grow linearly or even super-linearly.
2. Data Approaching the Ceiling: High-quality corpora on the public internet have been heavily reused. Synthetic data can supplement this, but improper quality control introduces distribution biases or risks degrading model capabilities.
3. High Cost of Failure: If an error occurs during a continuous training run spanning weeks, the financial and time costs of rolling back and restarting are unbearable.

Therefore, the cost-effectiveness of post-training—“digging deeper into capabilities on an existing base model”—is starting to surpass that of “building a larger base model.” This does not mean pre-training is unimportant; the knowledge breadth and potential ceiling of the base model are still determined by pre-training. Rather, the center of gravity for industry investment is shifting.

III. Code: The Natural Training Ground for Reinforcement Learning

Why is the breakthrough for post-training so often found in the coding domain? Because coding tasks possess three scarce characteristics:

• Objective Reward Signals: Whether a unit test passes or a compilation succeeds is a binary outcome, requiring no human scoring.
• Automatable Verification: Running code in an isolated sandbox provides immediate feedback without human intervention, and this process can be parallelized at scale.
• Complete Correction Loop: Error messages themselves serve as high-quality improvement guides. The model can continuously iterate through a “execute—observe error—correct—re-execute” cycle.

This is in stark contrast to RLHF. RLHF relies on a reward model trained on human preference rankings. If this reward model deviates from true human preferences, the policy will over-exploit it (a classic scenario of Goodhart’s Law). Conversely, RL with verifiable rewards is scored directly by the environment, providing an unbiased signal. This allows the training scale to expand almost indefinitely—which is the true meaning of “large-scale reinforcement learning.”

The core loop can be summarized with a conceptual pseudo-code snippet:

# Core loop of reinforcement learning with verifiable rewards (conceptual)
for step in range(total_steps):
    task   = sample_task(task_pool)       # Sample a task with verification cases
    traj   = policy.rollout(task.prompt)  # Model generates a solution trajectory
    result = sandbox.run(traj.code)       # Execute in an isolated sandbox
    reward = verify(result, task.tests)   # Case pass rate → reward signal
    trainer.update(policy, traj, reward)  # Policy gradient update

The brilliance of this loop lies in the fact that the reward is not given by a human, but “calculated” by the environment.

IV. From Code to Professional Tasks: Conditions for Capability Transfer

Generalizing the successful paradigm of coding to other professional tasks is not blind extrapolation. It holds true only if the target tasks meet three conditions:

1. Formalizable Objectives: The task has objective success criteria rather than subjective preference judgments.
2. Automatable Execution: There are sandboxes, simulation environments, or toolchains capable of automatically running the model’s outputs.
3. Computable Verification: Success or failure can be determined by a program—test results, schema validation, output comparison, numerical tolerances, etc.

Professional scenarios that meet these conditions are actually quite broad:

• Data Engineering: ETL scripts and SQL tuning can be verified using execution results and query plans.
• Software Testing: Test case generation naturally features quantitative metrics like pass rate and coverage.
• Ops Troubleshooting: Scripts and change plans can be dry-run and verified in simulation environments before deployment.
• Document Processing: Structured extraction can be automatically scored using field constraints and format validation.
• Numerical and Reporting: Financial cross-checking and formula recalculation provide precise right/wrong signals.

Conversely, for open-ended tasks that are difficult to verify automatically (such as creative writing or strategic consulting), directly applying this paradigm will fail. A hybrid route of “reward model + manual spot-checking” must be used instead. To determine if a domain is suitable for this training method, simply ask: Can its output be auto-graded by a program?

V. Engineering Practices: Four Key Points of Large-Scale Post-Training

1. Task Data Construction: The difficulty distribution of the task pool must form a gradient. If tasks are too easy, the model learns nothing; if too hard, the policy is prone to collapse. Data contamination must also be strictly prevented—overlap between training and evaluation tasks will inflate metrics artificially. In practice, teams build independent held-out task sets that are strictly isolated during training.
2. Reward Function Design: This is the most fragile part of the pipeline. Once the reward deviates from the true objective, the model will precisely exploit the loopholes (reward hacking): tampering with test results, forging execution logs, or outputting opportunistic “superficially correct” answers. Defensive measures include combining outcome rewards with process rewards, adding penalty terms for abnormal execution paths, and conducting regular manual audits of high-reward samples.
3. Infrastructure: Large-scale rollouts mean tens of thousands of sandboxes executing concurrently, putting immense pressure on compute scheduling, container orchestration, storage throughput, and log collection. Leveraging general cloud capabilities can significantly lower the barrier to entry: container platforms handle the elastic orchestration of sandboxes, object storage accommodates trajectory data and checkpoints, and logging and monitoring services track training health. For teams where self-building is cost-prohibitive, utilizing general-purpose cloud products for containers, storage, and compute—such as those offered by Baidu AI Cloud—is a common engineering choice in the industry.
4. Evaluation System: Reward metrics during training do not equate to real-world capability. A combination of independent evaluation benchmarks, cross-version regression testing, and canary releases in real business environments is required to avoid “metric-gaming” progress.

VI. Challenges and Boundaries

• Reward Fidelity Determines Capability Limits: If the reward design is flawed, the larger the training scale, the further the model deviates from the intended goal.
• Cost of Building Verification Environments: Many long-tail professional domains lack “ready-made unit tests.” Engineering effort must first be invested to build executable, scorable simulation environments.
• Security Isolation: Allowing models to execute code in an environment necessitates strict network isolation, resource quotas, and least-privilege access to prevent unauthorized actions during training.

Conclusion

Pre-training determines how deep a model’s foundation is, while post-training determines how far it can go. As the scaling dividends of pre-training slow down, the next phase of competition will focus on: how to use verifiable task loops to push models from “knowing how to write code” to “actually completing and correctly executing a full set of professional work.” For developers, understanding this “generate—execute—verify—correct” training logic will also help them more accurately evaluate and utilize the models at their disposal.