Post-Training in Practice: How to Unlock the Potential of Trillion-Parameter LLMs

1. Background: The Main Battleground for Capability Gains Is Shifting from Pre-Training to Post-Training

Over the past few years, improvements in large model capability have come mainly from scaling up pre-training—more data, more compute, more parameters. But this path now faces three constraints:

1. High-quality corpora are drying up: Publicly available high-quality text data has been consumed repeatedly, and the marginal value of newly added data is declining.
2. The cost-benefit gap is widening: Once parameter counts reach the trillion level, each additional percentage point on benchmark scores requires exponentially more compute.
3. Inherent limitations of pre-training: Next-token prediction excels at “knowledge compression” but falls short at “behavior shaping”—the model knows the answer, yet may not reliably, properly, or tool-assistedly produce it.

As a result, the industry’s focus has gradually shifted to post-training. Post-training refers to systematically shaping a model’s reasoning, tool use, and task execution capabilities on top of a pre-trained base model, through supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and, more recently, reinforcement learning with verifiable rewards (RLVR).

A notable industry signal: an increasing number of flagship models adopt “minor version” naming—retaining the existing base model and achieving capability leaps by substantially increasing post-training compute. This shows that post-training is no longer a “supplementary step” to pre-training, but the main engine for unlocking base model potential. Trillion-parameter base models, million-token context windows, and native multimodal understanding—these “hardware conditions” accumulated during pre-training all need post-training to “ignite” them.

2. Why Post-Training Can “Unlock” Base Model Potential

2.1 A Base Model Has “Potential,” Not “Capability”

A model fresh from pre-training has absorbed vast knowledge and strong pattern generalization, but these exist as “potential”: it will not reliably follow output formats, is not good at multi-turn tool calling, and does not understand the goal constraints of long-horizon tasks. Post-training’s job is precisely to “activate” this potential into stable, controllable, verifiable output.

2.2 Reinforcement Learning Explores the Capability Boundary

Unlike SFT’s “imitation learning,” RL allows the model to actively explore the solution space. A common diagnostic metric is pass@k: sample the same problem k times and observe whether any attempt succeeds. If pass@k is high but pass@1 is low, the base model “can do it but does so inconsistently”—exactly the problem post-training is best at solving: moving success from the tail of the distribution to the head, turning occasional correctness into stable capability.

2.3 Verifiable Rewards Make Feedback Scalable

The core of RL is the quality of the reward signal. In verifiable domains, rewards can come from objective execution results: whether unit tests pass for coding tasks, whether math answers are correct, whether a tool task’s final artifact meets specifications. Compared with human-labeled preference data, verifiable rewards are cheap, low-noise, and infinitely regenerable—which is precisely why RL can scale to hundreds of downstream domains.

3. Scaling Reinforcement Learning to Hundreds of Downstream Domains

3.1 Task Pool Construction and Curriculum Design

Covering hundreds of domains starts with answering “where do tasks come from”:

• De-identified extraction from real tasks: Distill high-frequency task patterns from real user requests to keep the distribution aligned;
• Programmatic synthesis: Use generators to build synthetic tasks with standard answers for rapid volume;
• Expert seed tasks: Domain experts write high-difficulty tasks to raise the capability ceiling.

Tasks are then graded by difficulty and clustered by domain to form curriculum-based training: easy first, hard later, preventing overly difficult tasks from disrupting the policy distribution early in training.

3.2 Layered Reward Function Design

Different domains have different reward sources. In engineering practice, rewards are usually structured in three layers:

def compute_reward(task, trajectory):
    r = 0.0
    # Layer 1: rule-based reward — format compliance, valid tool calls, parseable output
    if is_well_formed(trajectory.final_output):
        r += 0.1
    # Layer 2: execution reward — test pass rate, artifact verification, simulation scores
    r += 0.6 * run_tests_pass_rate(task, trajectory)
    # Layer 3: judge model — enabled only for open-ended tasks
    if task.open_ended:
        r += 0.3 * judge_model_score(task, trajectory)
    # Suppress reward hacking: penalize meaningless excessive retries
    r -= 0.05 * excessive_retry_penalty(trajectory)
    return clip(r, 0.0, 1.0)

3.3 Multi-Task Balancing and Preventing Capability Regression

Training on hundreds of domains simultaneously easily triggers the “seesaw effect”: math gets stronger while writing gets weaker. Common engineering countermeasures include: maintaining a cross-domain regression evaluation set and running full regression before each iteration; dynamically adjusting sampling weights by domain, tilting toward regressing domains; and using KL regularization to keep the policy from drifting too far from the reference model.

4. Four Infrastructure Pillars Supporting Large-Scale RL

4.1 Training Framework: Asynchronization and Compute Reuse

RL training is a “generate–evaluate–update” loop in which inference (rollout) and training (update) alternate. Serial execution causes large swings in GPU utilization. Modern frameworks decouple inference and training roles and pipeline them asynchronously, letting rollout and parameter updates overlap, while using techniques such as importance sampling correction to mitigate the policy staleness introduced by asynchrony.

4.2 Long-Context Inference

Million-token contexts make the memory footprint and latency of attention computation a bottleneck. Common industry optimizations include: sequence parallelism and context parallelism to split long sequences across multiple GPUs; paged KV cache management and lifecycle optimization; hybrid use of sparse attention and sliding windows; and trade-offs between recomputation and memory offloading.

4.3 Sandbox Environments: Execution Containers for Real Tasks

To let a model “modify code, run tests, and keep adjusting based on errors,” you must provide a safe execution environment:

• Container-level isolation, with network, filesystem, and resource quotas restricted;
• Snapshot and rollback mechanisms to support state restoration across multi-turn interactions;
• Pre-built toolchain images (compilers, test frameworks, modeling script interfaces, etc.);
• Structured return of execution results, serving both as a reward signal and as the basis for next-step decisions.

4.4 Task Scheduling System

Thousands of parallel rollouts, hundreds of domains, and trajectories of varying lengths put schedulers under severe strain: they need heterogeneous task queues bucketed by estimated runtime and GPU memory footprint; priority policies that give hard, low-pass-rate tasks more sampling budget; and elastic shifting of capacity between training and inference resource pools.

5. Feedback Loops in Real Tasks: Three Typical Scenarios

5.1 Coding: From “Code Completion” to “Long-Horizon Engineering”

The evaluation of real terminal coding tasks is quite representative: give the model a terminal environment and a task description, and let it autonomously locate code, make changes, run tests, and keep adjusting based on errors until everything passes. These tasks test long-horizon multi-step decision-making—every observation (error stack, test output) becomes new context, error recovery matters more than one-shot correctness, and evaluation happens in a real terminal rather than a closed dataset, which effectively rules out memorized answers.

5.2 3D Modeling and Game Scene Development

When calling CAD-type design tools, script interfaces of 3D modeling software, and game engines, the model needs to understand parametric modeling logic: build the skeleton first, adjust materials next, then render for verification. Rewards come from render result verification or geometric constraint checks. These tasks extend RL from pure text space into a “tool–environment” interaction space.

5.3 Financial Document Analysis

Long documents, table-dense content, and specialized terminology test long-context understanding and structured extraction. Verification mechanisms typically include: traceability checks between extracted fields and the source text, cross-table data consistency checks, and spot checks by domain experts. This shows that the benefits of post-training are not limited to code—they also cover knowledge work in vertical industries.

6. Challenges and Recommendations

Main challenges: reward hacking (the model exploiting loopholes in the reward function instead of actually solving problems), insufficient exploration efficiency in long-tail domains, benchmark contamination where evaluation questions leak into pre-training corpora and distort benchmarks, and post-training compute investment approaching that of a mid-scale pre-training run.

Recommendations for technical teams:

1. Build evaluation before training: Without a trustworthy evaluation set, RL is blind optimization;
2. Start with verifiable domains: Prioritize scenarios such as code and data extraction where rewards are easy to construct;
3. Leverage cloud infrastructure: General capabilities offered by cloud platforms such as Baidu AI Cloud—GPU compute, container services, logging, and monitoring/alerting—can significantly reduce the cost of standing up sandbox environments and training pipelines;
4. Iterate in small steps: Retain the stable base model and iterate post-training through versioned releases, making capability attribution and quick rollback easier.

7. Conclusion

The center of gravity in large model competition is shifting from “whose base model is bigger” to “whose post-training is more efficient.” Pre-training determines the ceiling of a model’s knowledge and potential; post-training determines how much of that potential gets realized by real users and real tasks. As the industry scales reinforcement learning to hundreds of downstream domains and builds feedback loops on real execution environments, “unlocking base model potential through post-training” is no longer a slogan—it is a reusable, iterable engineering methodology.