Post-Training: Unlocking the Potential of Trillion-Parameter Large Models

Over the past two years, the dominant narrative in the large language model (LLM) industry has been “scaling up pre-training”: more data, more compute, and larger parameter counts. However, a clear recent technical signal shows that the capability leaps in flagship models are increasingly driven not by starting pre-training from scratch, but by conducting “deep post-training” on existing foundation models. Through large-scale reinforcement learning (RL), domain task expansion, and accompanying infrastructure optimizations, the dormant potential within trillion-parameter foundation models is finally being transformed into deliverable product capabilities. Understanding this technical path is highly valuable for developers evaluating models and enterprises planning their AI investments.

1. Why Post-Training: From “Larger Models” to “Deeper Training”

1. Diminishing Marginal Returns in Pre-Training
Pre-training scaling follows scaling laws, but real-world constraints are becoming increasingly apparent. High-quality public corpora are nearing their extraction limits, and the quality and diversity of synthetic data cannot be recursively infinite. General language and knowledge benchmarks are saturating year over year, with the gap between top-tier models continuously narrowing. Furthermore, the cost of a single 10,000-GPU-scale pre-training run is enormous, making the price of trial and error massive. In other words, while continuing to scale pre-training remains necessary, the incremental capabilities it brings are shifting from “generational leaps” to “gradual improvements.”

2. Foundation Capabilities are “Latent,” Not “Deliverable”
Pre-training equips models with knowledge reserves and the rudiments of reasoning, but in their raw form, they often “talk” well without “doing” well. They struggle to stably invoke tools, maintain goal consistency in multi-step tasks, and adhere to vertical domain operational standards. These are precisely the capabilities most needed in enterprise scenarios. The role of post-training is to “activate” and “shape” the foundation model’s latent capabilities into stable, controllable, and verifiable engineering capabilities.

3. The Economics of Post-Training
Compared to retraining a foundation model, post-training offers three economic advantages. First, it reuses the foundation investment, shortening iteration cycles from months to weeks. Second, it allows for targeted reinforcement by domain and task, making ROI measurable. Third, failures can be rolled back and iterated, resulting in much lower engineering risk than a completely new pre-training run. Recent upgrade practices among top-tier models confirm this: a significant portion of capability improvements comes from multiplying post-training compute rather than swapping out the foundation model.

2. The Post-Training Tech Stack: A Four-Tier Progression

1. Supervised Fine-Tuning (SFT): Cold Start and Formatting
SFT uses high-quality “instruction-response” pairs to teach the model “how to do things right”: output formats, tool invocation protocols, and multi-turn dialogue norms. Its value lies in cold starts, providing a starting point with a reasonable behavioral distribution for subsequent RL. Its limitations are equally clear: it can only mimic demonstrations and cannot surpass them, leaving it powerless to “explore for better solutions.”

2. Preference Alignment: Making “Good” an Optimizable Objective
RLHF and direct preference optimization (DPO) methods use preference data from human or model feedback to establish stable judgments in the model for “choosing the better of two good answers.” This layer addresses subjective quality: tone, safety, and helpfulness. However, preference signals struggle to precisely measure whether “code actually runs” or “the reasoning chain is strictly correct,” leaving room for the next layer.

3. Reinforcement Learning with Verifiable Rewards: The Second Capability Leap
For tasks like coding, math, and structured extraction, objective grading standards exist: whether test cases pass, whether answers are correct, and whether field extraction is compliant. RL driven by verifiable rewards (often referred to as RLVR) allows models to autonomously explore strategies stronger than demonstration data through massive trial and error. Reward function design is the core engineering challenge at this layer:

def code_reward(submission: str, task_id: str) -> float:
    """Run submitted code in an isolated sandbox and score by test pass rate"""
    with Sandbox(image=f"task-env:{task_id}", timeout=60) as sb:
        result = sb.run(
            files={"solution.py": submission},
            cmd="python run_tests.py",
        )
    if result.exit_code != 0:
        return 0.0                # Compilation or runtime failure: zero reward
    passed, total = parse_test_report(result.stdout)
    return passed / total         # Partial pass gives partial reward, mitigating reward sparsity

Three key design points:

• Partial rewards: Only rewarding a full pass leads to overly sparse rewards; scoring by test case pass rate provides a smoother gradient signal.
• Preventing “reward hacking”: Models might learn to exploit grading rule loopholes (e.g., hardcoding outputs), requiring adversarial test cases and output diversity validation as fallbacks.
• Grading consistency: The sandbox environment must be version-fixed and reproducible; otherwise, reward noise will directly contaminate the policy gradient.

4. Domain Expansion: Pushing RL to Hundreds of Downstream Domains
This is the current main battlefield of post-training competition. The idea is to scale RL from a few “easy-to-grade” benchmarks to a task pool covering hundreds of downstream domains: multi-step Agent tasks (retrieval, browsing, tool orchestration), professional document analysis (long contracts, financial reports, research reports), 3D modeling and game scene development (invoking 3D modeling software, CAD tools, game engines to complete modeling and scene setup), and long-cycle software engineering (locating bugs in real code repositories, cross-file refactoring, passing regression tests). The difficulty of domain expansion lies not in algorithms but in environment construction. Each domain requires a task environment that is executable, gradable, and scalable for trajectory production, utilizing curriculum learning to organize tasks from easy to hard and preventing the model from spinning its wheels on overly difficult tasks.

3. The Engineering Foundation for Scale: Post-Training’s “Invisible Battlefield”

The bulk of post-training compute is often not in the “training” itself, but in “sampling.” Supporting parallel RL across hundreds of domains requires a complete infrastructure.

1. Training Framework: Decoupling Generation and Training
Each RL iteration involves three steps: “sample trajectories - compute rewards - update policy.” Trajectory generation throughput is often the bottleneck. The mainstream approach is to decouple and asynchronously pipeline the deployment of generation clusters and training clusters: the generation side continuously produces trajectories, while the training side consumes them in batches. Coupled with expert parallelism and load balancing optimizations in Mixture-of-Experts (MoE) architectures, communication overhead is kept within a controllable range.

2. Long Context: Million-Token Capabilities and Costs
Supporting million-token contexts requires sequence parallelism, progressive context length extension, and an appropriate ratio of long document data on the training side. On the inference side, meticulous calculation is needed for KV cache management, prefix cache reuse, and attention computation optimization. Otherwise, long context is merely “able to fit in” rather than “affordable and accurate.” For enterprises, long context and Retrieval-Augmented Generation (RAG) are not mutually exclusive: the former ensures global understanding, while the latter controls cost and timeliness. They are typically used in combination.

3. Sandbox Environments: Isolated, Reproducible, Scalable
Agent and code-based RL require models to actually execute: run code, call tools, operate browsers. Sandboxes must simultaneously provide security isolation (preventing side-effect spillage), rapid creation and destruction (supporting massive parallel sampling), and snapshot recovery (resuming long tasks from breakpoints). It can be said that “the environment is the data.” The quality and diversity of the environment directly determine the upper limit of RL capabilities.

4. Task Scheduling System: Orchestrating Mixed Workloads
Hundreds of domain tasks, heterogeneous reward services, and fluctuating generation loads require a unified scheduling layer. This layer allocates compute quotas by domain and difficulty, handles automatic retries and degradation for failed tasks, and balances throughput and fairness between long and short tasks. These systems are usually invisible but determine whether post-training can stably “scale up volume.”

4. Inference Delivery: One Capability, Multiple Forms

After post-training, the delivery format equally impacts the final experience. A common industry practice is to provide multiple inference tiers based on the same underlying capabilities. Through speculative decoding, quantization, KV cache optimization, and request scheduling, a “high-speed version” multiplies output speed to serve high-concurrency, interactive scenarios. The full-precision version targets scenarios extremely sensitive to quality. Both share the same model capabilities, with differences only in inference strategy, which requires inference optimization to “increase speed without dropping capability.” Additionally, the combination of native visual understanding and long context is expanding applicability from “reading text” to “viewing blueprints, reviewing layouts, and analyzing reports.” Open model weights also allow enterprises to privately deploy and deeply customize within their own environments, meeting requirements for data residency and compliance auditing.

5. Practical Advice for Enterprise Teams

1. Evaluation First, Targeting Real Tasks
General leaderboards only reflect a rough baseline. The real basis for decision-making should come from evaluation sets closely aligned with your own business: real programming tasks in terminal environments, professional document Q&A, and multi-step Agent tasks with step budgets. Evaluations must be automated, reproducible, and simultaneously record costs (steps, token consumption) to form a “capability-cost” two-dimensional view:

# Automated evaluation loop for real tasks (illustrative)
report = EvalReport()
for task in eval_suite:                    # Covers coding, doc Q&A, multi-step Agent
    traj = agent.solve(task, max_steps=task.budget)   # Limit steps to reflect real costs
    score = verifier.score(task, traj)     # Rule/test grading to avoid subjective drift
    report.add(task.id, score=score, cost=traj.steps)
print(report.summary())                    # Output combined capability-cost view

2. Follow a Progressive Logic for Method Selection
Prompt engineering and RAG come first due to low cost and quick results. Move to SFT when behavioral norms, formats, or domain terminology need stable solidification. Only consider RL when objective grading standards exist for tasks and demonstration data has hit its ceiling. Skipping levels (using RL to solve a problem that prompts could handle) is a common waste of budget.

3. Spend Budget on Environments and Rewards
For most teams, the bottleneck in RL failure cases lies not in algorithms but in data and environments: high reward noise, narrow environment coverage, and task distributions disconnected from real business. It is recommended to allocate a significant portion of investment to building high-quality task environments and grading systems.

4. Leverage Platform Capabilities to Lower Engineering Barriers
From data labeling, fine-tuning, and evaluation to deployment monitoring, post-training is a long pipeline. Model service platforms, such as Baidu AI Cloud, generally provide general capabilities for fine-tuning, evaluation, and deployment. Combined with foundational components like object storage, logging services, and monitoring alerts, teams can focus on genuinely differentiated work like “domain environment and reward design” rather than reinventing the training framework wheel.

6. Conclusion

The rise of post-training marks a shift in LLM competition from “making the foundation larger” to “making the most of the foundation.” Parameter scale determines the capability ceiling, while post-training determines the actual usable height beneath that ceiling. The engineering infrastructure—training frameworks, long context, sandboxes, and scheduling—serves as the bridge turning algorithmic concepts into stable product capabilities. For developers, understanding this path is essential to accurately evaluating the true quality of a model’s capabilities. For enterprises, choosing the right entry point into post-training is often more decisive for AI ROI than chasing the “largest parameters.”