Post-Training: How to Unlock the Real Potential of Trillion-Parameter Large Models

1. A New Paradigm for Capability Gains: Same Base Model, More Post-Training Compute

For years, the dominant path to better large models was more parameters and more pretraining data. But that path is now running into three bottlenecks: high-quality public text is becoming scarce, training costs are rising exponentially, and pretraining mainly learns general knowledge and language patterns rather than directly usable downstream capabilities.

At the same time, a notable industry signal has emerged. Several recent flagship model iterations did not rely on a brand-new, larger pretraining run. Instead, they kept the existing base model and substantially increased post-training compute, extending reinforcement learning across hundreds of downstream domains while upgrading the training framework, long-context reasoning, sandbox environments, and task scheduling systems. Repeated industry practice has shown that this approach alone can deliver visible gains in code generation, agents, 3D modeling, and professional document analysis.

In short: pretraining determines the model’s “knowledge ceiling,” while post-training determines how well the model can actually use that knowledge.

2. Why Post-Training Can Unlock Dormant Capabilities

A trillion-parameter base model has already compressed enormous world knowledge during pretraining, but that knowledge exists as static weights. To make the model reliably apply that knowledge in real tasks, it must move from “knowing” to “doing.” That behavioral shaping is the core value of post-training.

1. Behavioral alignment: Align output format, style, and instruction-following behavior with human expectations, addressing the problem of models that can answer questions but cannot complete real work.
2. Capability reinforcement: Use reinforcement learning to repeatedly try, fail, and improve on verifiable tasks, turning “roughly correct” into “correct on the first attempt.”
3. Domain expansion: Extend reinforcement learning to hundreds of downstream domains, with task-specific reward functions and verification mechanisms for each domain. This systematically amplifies the base model’s general capabilities and gradually extends them into natively multimodal directions such as document and chart understanding.

Take programming as an example: the model must actually modify code, run tests, and adjust based on error messages. 3D modeling and financial research require their own tool environments and validation mechanisms. This closed loop of “attempt, feedback, correction” trains engineering capability far more effectively than simple next-token prediction.

3. Post-Training Techniques: From Supervised Fine-Tuning to Agentic Reinforcement Learning

3.1 Supervised Fine-Tuning (SFT): Cold Start and Format Shaping

Post-training usually starts with supervised fine-tuning: fine-tuning the base model on high-quality instruction-response pairs so that it learns conversation format, tool-calling protocols, and basic behavioral norms. The quality of SFT data matters far more than quantity; a few thousand carefully curated samples often outperform hundreds of thousands of noisy ones.

3.2 Reinforcement Learning from Human Feedback (RLHF): Aligning Subjective Preferences

RLHF uses a reward model to rank candidate responses and then updates the policy with algorithms such as proximal policy optimization, making outputs more aligned with human preferences. It is good at subjective dimensions that are hard to quantify, such as tone, level of detail, and safety. However, for objective tasks like coding, human annotation is expensive and inconsistent.

3.3 Reinforcement Learning with Verifiable Rewards (RLVR): The Main Engine of the Current Leap

The core idea is: for any task whose result can be objectively verified, design a verifiable reward—unit tests passing, successful compilation, constraint satisfaction, or answer matching a reference—so the model can explore autonomously and use environment feedback instead of human labels. A simplified agentic RL training loop looks like this:

for episode in task_stream:              # continuously sample tasks from hundreds of domains
    state = env.reset(episode)           # initialize sandbox: code repo / document / tool environment
    trajectory = []
    while not done:
        action = policy(state)           # model generates next action (edit / command / tool call)
        state, reward, done = env.step(action)   # sandbox executes and returns feedback
        trajectory.append((state, action, reward))
    update_policy(trajectory)            # update policy using verifiable rewards

The key point is that rewards come from the environment, not from humans. When the model modifies code, the sandbox actually runs it. Failed test output enters the context, and the model continues fixing issues based on that feedback. This is the root cause of rapid improvement in terminal coding and multi-step engineering tasks.

4. Four Infrastructure Pillars for Scaling Post-Training

Scaling reinforcement learning to hundreds of domains is no longer about a single algorithm. It is a systems engineering challenge.

1. High-throughput training framework. Sample generation in agentic RL is far more expensive than in traditional training: a single trajectory can involve dozens of interaction rounds. The framework must support decoupled asynchronous generation and training, large-scale parallel sampling, trajectory caching, and failure replay. Otherwise, compute utilization can fall to unacceptable levels.
2. Long-context inference engine. Processing an entire code repository, a lengthy professional document, or multi-round tool interactions requires million-token context capability. On the inference side, this calls for chunked prefill, KV cache reuse, and context compression. On the training side, it requires addressing the memory and communication pressure caused by attention over long sequences.
3. Sandbox and tool environments. Verifiable rewards require executability. Coding tasks need isolated sandboxes with dependencies. 3D modeling and game scenario development need interface layers that can drive modeling software, engineering design tools, and game engines. Financial research needs document parsing and numerical validation pipelines. The realism and stability of the environment directly determine the quality of the training signal.
4. Task scheduling and fault tolerance. Hundreds of domains and tens of thousands of concurrent trajectories require a scheduling system that can dynamically allocate compute per domain, isolate faulty environments, and support checkpoint recovery. Sandbox crashes, timeouts, and resource contention are routine during training. Fault tolerance determines how much effective training time is actually achieved.

5. Validating Capability: Evaluations That Reflect Real Work

The gains from post-training ultimately need to be measured against evaluations that resemble real work. Common dimensions include:

• Terminal coding tasks: Given a real repository and requirement description in a sandbox, evaluate end-to-end test pass rates rather than function-level completion accuracy.
• Professional document analysis: Evaluate long-document understanding and numerical reasoning on professional benchmarks in areas such as finance and law.
• Long-horizon multi-step tasks: Evaluate whether the model can maintain goal consistency across dozens of tool calls, which directly corresponds to agent and automated engineering scenarios.

Be wary of benchmark overfitting: when reward functions and evaluation sets are highly similar in origin, rising scores do not necessarily mean real capability gains. In practice, keep an isolated test set that was never used in training, and supplement automated evaluation with human review and online controlled comparison.

6. Practical Advice for Enterprise Adoption

1. Choose a good base model rather than insisting on pretraining from scratch. Post-training on a mature general-purpose base model captures most of the benefit at significantly lower cost. This has repeatedly proven to be the highest-ROI path.
2. Put data engineering before reward design. First identify business tasks whose outcomes can be verified automatically, such as script execution, report generation, or configuration changes. Then design reward functions. A task pool that can be scored automatically is far more valuable than an equivalent volume of human preference data.
3. Use general cloud capabilities to quickly build the training foundation. Post-training involves large-scale GPU scheduling, containerized sandboxes, object storage, and log monitoring. Enterprises can rely on cloud providers such as Baidu AI Cloud for compute, container platforms, storage, and observability capabilities, avoiding the need to build infrastructure from scratch and keeping resources focused on data and reward design.
4. Reserve room for throughput optimization at deployment. Capability and speed can be decoupled. Quantization, speculative decoding, and output caching can significantly improve inference throughput with almost no loss in capability, meeting high-concurrency production requirements.

7. Conclusion

As the returns from parameter scaling and pretraining data begin to plateau, post-training is becoming the main competitive arena for large model capability. The lesson from recent industry practice is clear: a sufficiently strong general-purpose base model, combined with verifiable reinforcement learning across hundreds of domains, supported by sandboxes, long-context infrastructure, and scheduling systems, can deliver a major capability leap without changing the base model.

For developers and enterprises, the value of this methodology is not about chasing new models. It points to a repeatable path: on top of general-purpose intelligence, use feedback from real tasks to build domain-specific capability.