From “Acquiring Knowledge” to “Applying It Effectively”: A New Capability Curve

In recent years, the dominant narrative for improving large language models (LLMs) has centered almost entirely around pre-training: larger parameter counts, more data, and increased compute. However, an industry consensus is forming that the marginal returns of pure pre-training scaling are diminishing. Meanwhile, another curve is rising rapidly: increasing investment in reinforcement learning (RL) during the post-training phase to transform the “latent capabilities” learned during pre-training into “explicit capabilities” that can reliably solve real-world problems.

Recent industry practices offer a representative example. A foundation model was recently updated without redesigning its architecture or conducting another massive round of pre-training. Instead, it released an enhanced version by significantly increasing post-training compute. By optimizing the RL framework, long-context reasoning, sandbox environments, and training scheduling, the model supported joint RL training across hundreds of downstream domains, achieving notable improvements in reasoning, programming, and complex problem-solving.

The signal here is crucial for all developers and enterprise users: A model’s capability ceiling depends not only on how much it learned during pre-training, but also on whether subsequent training can translate that knowledge into stable, verifiable, end-to-end problem-solving abilities.

The Three-Stage Training Paradigm: Roles and Outcomes

To understand why post-training is so critical, we must clarify the three stages of LLM training and their respective objectives.

1. Pre-Training: Solving “What the Model Knows”

Through massive amounts of text, code, and multimodal data, pre-training uses self-supervised objectives like next-token prediction to teach models linguistic patterns, knowledge associations, code structures, and a degree of latent reasoning. It answers the question: What does the model understand, what does it know, and what potential does it hold?

However, a model that has only completed pre-training often “knows but cannot apply.” It may have read vast amounts of code but, in real-world development, might change a single file without considering its impact on other modules. It can write seemingly valid implementations but won’t proactively run tests to verify them. It can explain algorithms clearly but fails on slight variations of a problem.

2. Supervised Fine-Tuning (SFT): Solving “Does It Follow Instructions”

SFT uses high-quality instruction-response pairs to teach the model task “formats”: how to answer questions, how to write code, and how to conduct multi-turn conversations. However, SFT’s ceiling lies in “imitation.” The model can only align with the distribution of the demonstration data. It performs well on covered scenarios but often fails on uncovered ones, and it struggles to surpass the quality of the demonstrations themselves.

3. Reinforcement Learning: Solving “Does It Do It Right”

Post-training RL takes a different path. Instead of giving the model standard answers to imitate, it allows the model to attempt tasks on its own, rewarding good outcomes and reinforcing behaviors that lead to successful results. It provides exactly what SFT cannot: the exploratory generalization capability across the solution space, and the stability to complete a task from start to finish.

Underlying Mechanisms: Why RL Significantly Enhances Capabilities

1. Transforming “Latent Capabilities” into “Explicit Capabilities”

The distribution of a post-trained model contains numerous possible solution paths, including correct ones, but the model itself does not know which is most reliable. RL is essentially a “search and amplify” process: sampling multiple candidate solutions for the same problem, using rewards to identify the correct ones, and gradually increasing the sampling probability of those successful paths. This explains why RL-trained models often exhibit emergent behaviors like self-checking, backtracking, and reflection. These behaviors likely already existed latently in the pre-training distribution; RL simply amplifies the probability of them being selected.

2. Verifiable Rewards Provide Objective, Scalable Optimization Signals

Traditional human-preference-based alignment suffers from subjective labeling, high costs, and susceptibility to reward hacking. The recent success of post-training RL in reasoning and programming scenarios is driven by the “verifiable rewards” paradigm. Whether a math final answer is correct can be programmatically determined, and whether code compiles and passes unit tests can be automatically executed. Reward signals thus become objective, automated, and mass-producible, creating the conditions necessary to scale RL training.

A conceptual reward function can be understood as follows:

def reward_fn(generated_code: str, task: Task) -> float:
    # Layer 1: Static check for format and syntax validity
    if not is_parseable(generated_code):
        return -1.0
    # Layer 2: Sandbox execution to run unit tests and verify correctness
    result = sandbox.run(
        code=generated_code,
        tests=task.unit_tests,
        timeout_s=30,
    )
    # Layer 3: Tiered rewards; partial pass gives partial credit to mitigate reward sparsity
    score = result.passed_ratio
    if result.timed_out:
        score -= 0.2   # Timeout penalty to guide model efficiency
    return score

3. From “Imitating Demonstrations” to “Surpassing Demonstrations”

SFT is limited by the quality of the demonstration data, whereas RL allows the model to find better solutions through exploration. Faced with complex tasks, a model might spontaneously learn to decompose the problem, verify intermediate steps, and cross-check code after writing it. These strategies might not appear verbatim in the demonstration data. As long as they yield higher rewards, they can be discovered through exploration and permanently solidified.

Engineering Foundations: Scaling Post-Training RL

Scaling post-training RL is not simply “running a training script”; it is a systems engineering endeavor. The aforementioned industry practices proved effective because of solid investments in several key areas:

1. High-Throughput RL Frameworks. RL training alternates between generation (rollout) and updating. Generation requires massive parallel inference, while updating requires efficient training. Designs that separate inference and training, asynchronously schedule samples, and reuse results directly determine training throughput and compute utilization, serving as the prerequisite for scaling investment.

2. Long-Context Reasoning Capabilities. As reasoning chains lengthen, the context for a single sample can expand from a few thousand tokens to tens of thousands or more. This places extreme demands on training stability, memory management, and inference acceleration under long contexts. The model must also learn to “think longer without going off-track”—a capability inherently shaped during post-training.

3. Sandbox Execution Environments. Training for programming tasks relies on real execution feedback. Model-generated code must be compiled, run, and tested in isolated sandboxes, involving resource limits, timeout controls, security isolation, and massive concurrent scheduling. Without stable sandbox infrastructure, verifiable rewards are impossible.

4. Multi-Task Joint Training. To apply RL simultaneously across hundreds of downstream domains, engineers must unify reward metrics, balance data ratios, and ensure capability equilibrium to prevent scenarios where the model improves in math but degrades in conversation. This is fundamentally a multi-objective optimization problem and represents the critical engineering gap between “single-domain RL” and “general-capability RL.”

5. Training Scheduling and Stability. Large-scale RL involves alternating roles (generation, evaluation, updating), frequent checkpointing, and fault recovery. Any instability is amplified at the cluster level. Mature scheduling and observability systems are hidden thresholds for scaling.

Implications: It’s Not Necessarily About “Bigger Models”

The post-training route offers a second path for capability iteration:

• More controllable cost and cycle time. Compared to redesigning architectures or rerunning pre-training, increasing post-training investment on an existing foundation model allows for faster iteration and more direct validation.
• The foundation model becomes a “capability container.” The reservoir of knowledge and potential is determined during pre-training, while post-training handles the “bottling”—packaging potential into specific task capabilities.
• More flexible versioning strategies. The same foundation model can spawn multiple versions tailored to different scenarios through varied post-training investments, matching differentiated deployment needs.

For enterprises, this means that evaluating a model shouldn’t rely solely on foundation model benchmarks. Post-training quality matters more: stability in instruction following, execution rate of generated code, and fidelity in long-context tasks are often the key variables determining the actual deployment experience.

How Enterprises Should Understand and Leverage Post-Training Capabilities

1. Evaluate based on “real task performance” rather than parameter scale. Build evaluation sets around your specific business scenarios. Focus on completion rates, execution rates, and stability on real tasks.

2. Choose the capability evolution path by stage. Prompt Engineering → Retrieval-Augmented Generation (RAG) → Fine-Tuning → Reinforcement Learning. Costs and benefits increase sequentially. Most business problems can be solved with the first two steps. Training is only worth the investment when core model behaviors need to be solidified and clear reward functions or high-quality data can be defined.

3. Leverage platform capabilities to avoid reinventing the wheel. Post-training involves extensive infrastructure, including data pipelines, training frameworks, evaluation systems, and sandbox environments, making in-house development costly. Mature, one-stop LLM development platforms (such as Baidu AI Cloud’s relevant services) have productized the entire workflow—data management, fine-tuning, evaluation, and deployment—allowing enterprises to focus their energy on business data and scenario design.

4. Establish continuous evaluation and regression mechanisms. Post-training is not a one-time action; models, data, and tasks continuously evolve. Building a regression evaluation set that covers core business functions and continuously tracking model performance ensures that capability improvements remain “measurable and manageable.”

Conclusion

The significance of post-training reinforcement learning lies in answering a fundamental question: How does an LLM’s “knowledge” become “capability”? Pre-training determines what a model knows; post-training determines how well it can execute. As pre-training scaling approaches the ceilings of data and cost, the post-training phase, centered on reinforcement learning, is becoming the primary battleground for LLM capability competition. For developers and enterprises, understanding this trend means understanding the true leverage point for the next stage of capability enhancement.