Post-Training at Scale: Unlocking the Potential of Trillion-Parameter Models

1. Background: The Center of Gravity Is Shifting from Pre-Training to Post-Training

For years, capability gains in large models came mainly from “bigger pre-training”: more data, more compute, more parameters. That path now faces three constraints:

1. High-quality corpus growth is plateauing. The usable增量 of public web text declines year over year, and the marginal returns from continuing to pile on data are diminishing明显;
2. Pre-training costs are rising exponentially. A full pre-training run of a trillion-parameter base model means months of compute investment and extremely high engineering risk;
3. A “last mile” gap exists between pre-training and real需求. A model “knowing the answer” does not mean it can reliably “do the right thing.”

Against this backdrop, a clear technical shift has emerged: rather than rushing to train larger base models, the industry is using larger-scale post-training—especially reinforcement learning—on existing base models to continuously unlock capabilities in the parameters that have not yet been fully activated.

One intuitive signal comes from version naming conventions: major version numbers usually correspond to a completely new pre-training run, while decimal version iterations often represent capability evolution of “the same base model with stronger post-training.” Behind this naming logic lies the maturation of the post-training scaling technical路线.

2. Why Post-Training “Releases” Rather Than “Adds” Capabilities

2.1 The Base Model Is a Capability Container; Post-Training Is the Activation Mechanism

The essence of pre-training is compressing statistical patterns from massive corpora into parameters. A trillion-parameter base model holds far more knowledge and reasoning potential than it exhibits by default. Post-training does not inject brand-new world knowledge into the model; it does three things:

• Reshape the output distribution: make the model more inclined to choose correct,规范, verifiable solution paths;
• Open up capability pathways: organize fragmented skills learned during pre-training (code completion, multi-step reasoning, tool use) into stable, reusable behavior patterns;
• Establish a feedback loop: use real task outcomes to correct the model’s self-evaluation, narrowing the gap between “thinking it’s right” and “actually being right.”

2.2 Scaling Laws Continue in the Post-Training Phase

The effectiveness of reinforcement learning continues to improve with growth in compute, number of tasks, and trajectory sampling scale, exhibiting scaling patterns similar to pre-training. The difference: pre-training scales “data × parameters,” while post-training scales “tasks × attempts × feedback quality.” For teams that already possess a strong base model, the cost-effectiveness of adding compute on the post-training side is often significantly higher than launching a larger pre-training run from scratch.

2.3 The Economics: The Compound Effect of Reusing the Base Model

The investment in a single pre-training run is “sunk” into the base model, and the base model can be reused across multiple rounds of post-training: each round of domain expansion and each iteration of the reward model amortizes the base model cost. This is precisely why leading teams in recent years have prioritized compute toward “reinforcement learning across hundreds of downstream domains” rather than rushing to start the next base model training run.

3. Deconstructing the Post-Training Technical System

An industrial-grade post-training upgrade is by no means just “running a few more rounds of reinforcement learning”—it is a systems engineering effort composed of tasks, environments, frameworks, and scheduling.

3.1 Domain Scaling: Extending Reinforcement Learning to Hundreds of Downstream Tasks

Reinforcement learning in the early alignment phase mainly covered a small number of general capabilities. The key change in the new generation of practice is extending it to hundreds of细分 domains: code generation and defect repair, mathematical derivation, long-document analysis, multi-step tool invocation, professional domain Q&A, and more.

The core enabler of this expansion is verifiable rewards:

• Tasks with verifiable outcomes (executable code, checkable math, validatable formats) are scored directly using execution results;
• Tasks that are hard to verify automatically (open-ended writing, solution design) use calibrated model judgments or human spot checks;
• Multi-step tasks introduce process rewards to avoid credit assignment distortion caused by只看最终结果.

A simplified reward function skeleton is as follows:

def compute_reward(task, trajectory):
    # 1) Static validation: output format, legality of tool calls
    if not validate_format(trajectory.output):
        return -1.0
    # 2) Sandbox execution: run code / invoke tools / replay action sequences
    result = sandbox.execute(trajectory.actions, timeout=600)
    if result.failed:
        return process_score(trajectory)   # Process score, encourages valuable exploration
    # 3) Result comparison: test cases / reference answers / numerical validation
    return 1.0 if verify(result, task.reference) else 0.0

A supporting “task factory” mechanism automatically extracts, rewrites, and augments task samples from real scenarios, forming a continuously supplied data flywheel. Task diversity and difficulty distribution directly determine the ceiling of reinforcement learning.

3.2 Long-Context Reasoning: From “Fitting It In” to “Using It Well”

Million-token context windows have gradually become a common configuration for flagship models, but “a big window” does not equal “using it well.” Typical long-context problems include attention dilution and “lost in the middle” information: when key information is buried tens of thousands of tokens deep, the model’s retrieval and citation accuracy drops noticeably.

Common training-side practices include:

• Staged extension: first train stably at medium lengths, then progressively enlarge the window while adjusting the positional encoding scheme;
• Long-document task construction: design tasks around ultra-long documents such as cross-segment extraction, global comparison, and structured summarization, so the model practices “needle in a haystack” during training;
• Multi-turn session state maintenance: simulate long-range dependencies in real Agent scenarios.

On the inference side, KV cache management and attention computation optimization are combined to keep latency and cost controllable under long contexts. Long context and Agent tasks are naturally coupled—only when the model can reliably leverage the full context do multi-step tool invocation and long-horizon tasks have a foundation.

3.3 Sandbox Environments: Letting Models Trial-and-Error with Real Feedback

The ceiling of post-training quality depends heavily on the realism of the environment. Static question-bank-style training easily leads to evaluation overfitting, while sandbox environments provide feedback signals close to reality:

• Code execution sandboxes: code submitted by the model actually runs in isolated containers, and passing tests earns points;
• Terminal and file system simulation: supports multi-step command operations, building and debugging engineering-grade repositories;
• Tool invocation simulation: encapsulates retrieval, computation, and interfaces for professional software such as 3D modeling and computer-aided design, covering real productivity scenarios.

In the sandbox, the model can “run—error—fix—run again” like an engineer, and reinforcement learning learns long-horizon problem-solving ability from these complete trajectories. This is precisely the infrastructure prerequisite for the rapid progress of Agent capabilities in recent years.

3.4 Training Frameworks and Task Scheduling: The Underestimated Deciding Factor

When reinforcement learning tasks expand to hundreds of domains and trajectory sampling reaches massive scale, the engineering framework becomes the actual bottleneck:

• Asynchronous generation: decouple trajectory generation from policy updates to avoid idle compute;
• High-throughput sandboxes: container pooling, snapshot recovery, and failure retry to ensure environment execution does not drag down training;
• Checkpoint resumption and elastic scheduling: in large-scale, long-duration training, failure recovery capability directly determines effective training time;
• Cross-domain compute scheduling: dynamically allocate training resources based on each domain’s pass rate, gradient signals, and business value, spending compute on tasks with the highest marginal returns.

It can be said that in the second half of the post-training race, what is being compared is not only algorithms but also the maturity of the entire infrastructure stack of “tasks—environments—training—scheduling.”

4. How to Scientifically Evaluate Post-Training Effectiveness

Post-training easily falls into “leaderboard-chasing optimization.” A scientific evaluation system needs to pay attention to three points:

1. Replace static question banks with real tasks. For example, complete defect repair on real code repositories in sandboxed terminal environments, perform cross-chapter information extraction and verification on ultra-long documents, and drive multi-step tools to complete end-to-end delivery. Such tasks correlate far more strongly with real workloads than static Q&A;
2. Strictly prevent data contamination. Deduplicate and isolate sources for evaluation sets, and retain completely non-public holdout tasks to avoid inflated scores caused by “memorizing the questions”;
3. Look at capability, cost, and speed simultaneously. The same base model can spawn faster lightweight versions through distillation, quantization, and inference optimization. When capability metrics are similar, throughput and latency are often the deciding factors in enterprise selection.

5. The Enterprise Perspective: How to Build Your Own Post-Training Loop

For enterprises hoping to reuse this path in their own business, the following layered approach can be referenced:

• Data layer: precipitate business tasks into verifiable samples (code tasks with test cases, document Q&A with standard answers)—this is the most important asset;
• Environment layer: build lightweight sandboxes for high-frequency scenarios, gradually replacing “human review” with “automatic verification + spot checks”;
• Training layer: start with supervised fine-tuning cold start, transition to small-scale domain reinforcement learning, then gradually expand task coverage;
• Evaluation layer: establish regression evaluations tied to online business metrics to prevent capabilities from trading off against each other;
• Infrastructure layer: training and inference have high requirements for compute, scheduling, storage, and monitoring. Enterprises can build on general capabilities such as GPU compute, container services, object storage, and logging and monitoring alerts from cloud platforms—for example, mainstream cloud platforms such as Baidu AI Cloud all provide such foundational components, and enterprises can combine them as needed without building from scratch.

A practice principle worth emphasizing: get the evaluation loop working first, then scale up reinforcement learning. Scaling training without reliable feedback signals will only amplify errors efficiently.

6. Challenges and Outlook

Post-training scaling is not a silver bullet, and several unresolved problems remain:

• Reward hacking: models may learn to exploit loopholes in the reward function, requiring continuous adversarial calibration;
• Capability seesaw: strengthening one domain may harm other capabilities, requiring mixed-domain training and regression evaluation as a safeguard;
• Catastrophic forgetting: after multiple rounds of post-training stack up, the base model’s original capabilities may degrade;
• Verifiability of open-ended tasks: a large amount of real work lacks objective scoring standards, and reward modeling remains a core difficulty.

Looking ahead, the division of labor—“pre-training lays the foundation, post-training builds the high-rise”—will become increasingly clear: base model iteration cycles will lengthen, post-training will continue rolling at higher frequency, and the growth curve of model capability will increasingly be written by “repeated attempts and feedback on real tasks.” For developers and enterprises, understanding and laying out this technical path early has more long-term value than chasing any single specific version release.