Post-Training: The Key Path to Unlocking the Potential of Trillion-Parameter Models

A notable signal has emerged in the large model space recently: flagship models from multiple vendors have made clear progress in coding, agentic tasks, and professional knowledge work—not through a brand-new, larger pre-training run, but by significantly scaling up post-training compute on top of an existing base model, extending reinforcement learning across hundreds of downstream domains. The industry has even coined the practice of “same base, ever-new flagship”: reusing the same generation of trillion-parameter base model—one that supports million-token context and native multimodal understanding—and turning it into a more capable flagship simply through post-training.

This technical path deserves serious attention from every developer and enterprise technology decision-maker. This article breaks it down systematically across four dimensions: why, how, what supports it, and how to use it well.

1. Pre-Training Returns Are Peaking: The Main Battlefield Has Shifted

1. Diminishing marginal returns on data and compute. The extraction of high-quality public corpora is approaching its ceiling. Each step up in parameter scale requires exponentially more data and compute, yet general capability gains are flattening. Continuing to simply “stack pre-training” yields steadily worsening returns on investment.

2. General capability does not equal task capability. A base model that has only undergone pre-training is like a knowledgeable new hire who has never worked a day on the job: it “knows” a lot, but how to decompose tasks, call tools, execute over long horizons, and self-correct within real workflows must be aligned and reinforced through dedicated post-training. Parameter scale determines the upper bound of intelligence; post-training determines how much of that range is actually realized.

3. Iteration cost and cycle differ enormously. A large-scale pre-training run takes months and costs a great deal; post-training can continuously refresh versions on an existing base at far lower cost, delivering new capabilities on a weekly or monthly cadence. This is the fundamental reason “base reuse + post-training upgrades” has become the industry’s mainstream rhythm.

2. The Post-Training Stack: A Pipeline, Not a Single Algorithm

Modern post-training consists of multiple stages that build on each other:

• Supervised Fine-Tuning (SFT): Uses high-quality “instruction–response” data to teach the model to follow instructions and produce stable output formats—the first step in alignment.
• Reinforcement Learning from Human Feedback (RLHF): Trains a reward model from human preference comparisons, then uses policy optimization algorithms such as PPO to make outputs better match human expectations. Suited to open-ended, subjective tasks.
• Reinforcement Learning with Verifiable Rewards (RLVR): For tasks with objective ground truth—code, math, structured extraction—the reward signal comes directly from “did the unit tests pass, is the answer correct,” bypassing the human annotation bottleneck. This is the core engine behind recent capability leaps.
• Process Supervision and Chain-of-Thought Training: Evaluates not just final results but the quality of intermediate reasoning steps, suppressing confident fabrication.

The core RLVR loop can be summarized in pseudocode:

for batch in task_stream:            # task distribution covering hundreds of downstream domains
    rollout = policy.generate(
        batch.prompt,
        tools=batch.tools,           # tool calls allowed inside the sandbox
        max_turns=batch.max_turns,  # multi-turn interaction, trial and error permitted
    )
    reward = verifier.check(rollout, tests=batch.tests)  # verifiable reward
    advantages = compute_advantage(reward, rollout.logps)
    policy.update(advantages)        # policy gradient update

This loop is simple to write but extremely expensive to run: every step involves large-scale rollout generation, sandbox execution, and reward settlement, consuming inference compute. Without sufficient sample volume and parallelism, policy updates drown in noise.

3. What Makes Scaling RL to Hundreds of Domains Hard

1. Reward function design. Verifiable tasks have clear rewards; open-ended tasks (document analysis, solution writing) require a combination of “verifiable proxy metrics + preference models,” otherwise the reward signal is too noisy for training to converge.

2. Reward hacking. Models naturally exploit loopholes: hardcoding test cases, pleasing the evaluator rather than actually solving the problem. Adversarial test sets and continuous auditing of reward functions are needed.

3. Domain mixing and the seesaw effect. In multi-domain mixed training, reinforcing one domain often degrades others. Common countermeasures include curriculum learning and dynamically adjusting task sampling ratios per domain based on capability performance.

4. Catastrophic forgetting. After heavy task reinforcement, a model’s general conversation and knowledge abilities may suffer. General data replay must be mixed in, striking a balance between “specialized reinforcement” and “generalist retention.”

4. The Four Infrastructure Pillars Supporting Post-Training

The ceiling of post-training is determined not only by algorithms but by the throughput and reliability of supporting systems:

1. High-throughput training frameworks. Rollout and parameter updates require asynchronous orchestration; heterogeneous scheduling that uses high-bandwidth compute for training and lower-cost compute for inference and sandboxes directly determines effective compute utilization and per-sample cost.

2. Long-context inference optimization. Million-token context means KV cache memory and memory access become bottlenecks. The industry commonly adopts paged caching, sparse and hierarchical attention, and similar optimizations to balance cost and latency—otherwise long-document tasks simply cannot run at scale.

3. Sandbox environments. For agents to “get their hands dirty” in real tasks, securely isolated execution environments are required: containerized isolation, filesystem and network quotas, and snapshot rollback are all indispensable. More critically, the environment must be realistic—the sandbox must contain real toolchains, business-relevant data, and dependencies so that trained capabilities transfer to production.

4. Task scheduling systems. Hundreds of thousands of parallel rollouts per day, fault detection and checkpoint recovery, result aggregation and reward settlement—these require a stable batch scheduling layer, essentially a “distributed job system purpose-built for training.”

Only when all four pieces are in place can a model trial-and-error, receive feedback, and iterate daily across massive real tasks, with capabilities climbing steadily over time.

5. Capability Validation: Look at Real Tasks, Not Leaderboard Scores

The effectiveness of the post-training path must ultimately land on observable task performance. The industry’s evaluation focus is shifting toward “real tasks”:

• Real terminal coding: Completing multi-step work such as environment setup, dependency installation, debugging, and deployment in a Unix-like environment—not just completing a single function;
• Professional document analysis: Information extraction, calculation, and cross-validation in long-document scenarios such as finance and legal, testing long-context utilization and the stability of structured output;
• Professional tool-driven tasks: Calling tools such as 3D modeling, CAD, and game engines to produce complex outputs, testing multimodal input understanding and toolchain orchestration;
• Long-horizon engineering tasks: Maintaining planning, memory, and error correction across dozens of steps and extended runtimes.

At the same time, beware of evaluation traps: public leaderboards may suffer from data contamination, and a high score on a single benchmark does not mean the model works well for your business. The most reliable approach is to build a private evaluation set close to your own business, run offline regression tests with real samples, and then decide whether to deploy.

6. Practical Advice: Turning Model Capabilities into Business Value

1. Select by “task profile,” not by parameter count. Design small-scale offline evaluations based on your own workflow (context length, tool calling, output format stability). Validate first, then scale.

2. Layer inference to control costs. Some vendors offer accelerated inference versions of the same model, with output speeds several times faster. Route complex planning to the deep reasoning version and high-frequency, latency-sensitive calls to the accelerated version, achieving cost tiering through routing:

route = "highspeed" if (task.is_routine and task.latency_sensitive) else "pro"
response = client.invoke(model=route, messages=task.messages)

3. Invest in context engineering. Million-token context does not mean you can dump everything in blindly; retrieval quality, instruction placement, and history compression strategies have an enormous impact on final results.

4. Give the model an executable environment. No matter how capable an agent is, it needs a clean sandbox, clearly defined tools, and unambiguous success criteria. A containerized execution environment, combined with logging services and monitoring alerts, significantly improves system observability and problem reproducibility.

5. Leverage general-purpose cloud platform capabilities. For example, the large model platform, container platform, object storage, logging, and monitoring services offered by Baidu AI Cloud can support the full chain from model invocation, fine-tuning, and sandbox setup to online operations. Enterprises can rapidly validate a model’s real value in their business without building infrastructure from scratch.

Conclusion

“Model capability gains increasingly depend on repeated trial and feedback in real tasks.” This is both a footnote to the post-training path and a revelation of what the next stage of competition hinges on: not just parameter scale, but high-fidelity task environments, reliable reward signals, and high-throughput training systems. For users, the best way to judge a large model’s true quality is simply this—put it into your real tasks and let it run.