Post-Training Takes the Lead: Unlocking the Potential of Trillion-Parameter LLMs

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

For the past several years, improving large model capabilities has been nearly synonymous with one thing: making pre-training bigger. More data, more compute, longer training cycles—pushing model parameters from hundreds of billions toward the trillion-parameter scale. But this path is now facing three constraints:

• Marginal exhaustion of high-quality corpora. Publicly available high-quality text has been repeatedly exploited, and the returns from simply piling on more data continue to diminish.
• Imbalance between cost and iteration cycle. A fresh round of large-scale pre-training takes months and enormous investment, making rapid capability iteration difficult to sustain.
• Insufficient realization of potential. Many failure cases stem not from the model “not knowing” but from execution-layer problems—unstable instruction following, broken reasoning chains, tool-calling errors. The phenomenon of base models that “can but don’t do it right” is widespread.

A clear trend has emerged: leading teams are no longer rushing to launch new pre-training rounds. Instead, they are building on existing strong base models and concentrating incremental compute on post-training—a combination of supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and reinforcement learning from verifiable rewards (RLVR). Recent public evaluation information shows that flagship models iterated along this route have approached or even reached industry-best levels in real-world terminal coding, complex document understanding, and cross-tool long-horizon tasks—proving that “keep the base, scale up post-training” is not a stopgap but a mature technical strategy.

2. Technical Logic: Why Post-Training Can “Cash In” the Potential of Trillion-Parameter Models

2.1 The Base Model Is “Stored Potential”; Post-Training Is the “Realization Mechanism”

During pre-training, the model absorbs vast world knowledge and code corpora, forming an enormous reservoir of latent capability. But these capabilities exist implicitly in the form of “next-token prediction.” Without guidance, they manifest as chaotic answer formatting, abandoned multi-step reasoning, and incorrect tool-call parameters. The essence of post-training is to use reward signals to systematically steer model behavior onto “verifiably correct trajectories.” An analogy: the base model is a candidate who has mastered all the knowledge points; post-training is intensive, targeted practice and calibration of answer conventions.

2.2 Verifiable Rewards: Making Reinforcement Learning Truly Scalable

Early RLHF relied on human preference annotation, which is costly, noisy, and vulnerable to reward hacking. But tasks like code, math, and structured analysis naturally have objective criteria: unit test pass rates, compilation success, final-result matching, simulation replay correctness. Rewards shift from “learned” to “computed,” dramatically reducing signal noise and manipulability. This is the fundamental prerequisite for scaling reinforcement learning to hundreds of downstream domains.

A minimal illustrative loop:

# Illustration: verifiable-reward-based RL main loop (pseudocode)
for task_batch in task_sampler.stream(domains=DOMAINS):
    # 1. Sampling: policy model generates full trajectories in sandboxes
    trajectories = policy.sample(
        task_batch,
        env=sandbox_pool.acquire(),   # isolated, rollback-capable execution environment
        max_ctx_len=1_000_000,        # million-token context window
    )
    # 2. Verification: rewards are "computed," not "learned"
    rewards = [verifier.score(t) for t in trajectories]
    # 3. Update: KL-constrained policy optimization to prevent behavior drift
    policy.update(trajectories, rewards, kl_budget=KL_TARGET)
    sandbox_pool.release_all()

2.3 Hundreds of Domains in Parallel: From “Alignment” to “Full-Spectrum Capability Development”

Extending RL from single-purpose dialogue alignment to hundreds of task domains—code repair, terminal operations, data analysis, professional documents, 3D modeling, and more—yields three benefits: capability transfer, where multi-domain training mutually reinforces generalization; overfitting resistance, avoiding the model becoming strong only on a single benchmark; and curriculum-based training, progressing along task difficulty gradients to steadily raise the capability ceiling.

3. Engineering Pillars: Four Infrastructure Foundations for Large-Scale Post-Training

At depth, post-training competition is really infrastructure competition. Four pillars are indispensable:

3.1 Training Framework: Throughput Determines Iteration Speed

RL training is a “sample—verify—update” loop, where trajectory rollout typically consumes most of the time. Key framework-level optimizations include decoupling inference engines from training engines, asynchronous pipelining to eliminate GPU idle time, and efficient transfer and reuse of trajectory data. Effective throughput (valid trajectories completed per GPU per second) directly determines “how many days it takes to complete one full-domain training round.”

3.2 Long Context: A Hard Prerequisite for Agent Tasks

Agent tasks naturally produce very long contexts: tool outputs, web content, code repositories, multi-turn interaction logs. Million-token context capability requires coordination across three dimensions: on the training side, long-sequence parallelism and attention optimization (general techniques such as sparse attention and KV cache management); on the inference side, cache scheduling and context compression; and on the capability side, “using it well”—precisely locating key information within very long content, maintaining cross-paragraph consistency, and resisting irrelevant information interference.

3.3 Sandbox Environment: The Physical Carrier of Verifiable Rewards

Executing code, running tests, operating terminals, rendering 3D scenes—all require sandboxes that are isolated, reproducible, and capable of high-concurrency spawning. Engineering essentials include containerized isolation, snapshots and state rollback, resource quotas, and escape-prevention security. It is worth emphasizing: sandbox stability directly determines reward quality—environment jitter injects noise into reward signals and poisons the entire training process.

3.4 Task Scheduling: Orchestrating Heterogeneous Loads into a Single Machine

Large-scale RL involves three types of workloads with very different resource profiles: policy inference is throughput-heavy, verification execution is IO- and concurrency-heavy, and parameter updates are communication-heavy. A unified scheduler must orchestrate task queues, sandbox pools, and GPU pools, supporting elastic scaling, failure retry, and priority scheduling—the underlying guarantee that hundreds of domains can train in parallel without breaking down.

4. Capability Evaluation: From “Answering Questions” to “Delivering Results”

Evaluation methods themselves are evolving alongside the technical route. A new generation of evaluation exhibits three characteristics:

1. End-to-end real environments. Models complete the full process—environment setup, defect localization, test passing—in containerized real command-line environments, scored on final deliverables rather than static multiple-choice questions.
2. Deep understanding of professional documents. Facing documents with complex layouts such as reports and contracts, evaluation tests comprehensive parsing, retrieval, and reasoning ability—a rigorous challenge for both multimodal understanding and long context.
3. Cross-tool long-horizon collaboration. Models are required to invoke professional toolchains—3D modeling tools, CAD-class software, game engines—to complete multi-step, iterable constructive tasks, testing planning ability and persistent context management.

From overall public evaluation trends, flagship models that have undergone large-scale post-training have narrowed the gap with traditionally strongest models on real terminal coding tasks to a razor’s edge, and have achieved leadership on some professional document understanding tasks. At the same time, the same capability base can spawn faster version tiers through inference optimization, balancing quality and cost. These observations collectively confirm: the depth of post-training is becoming a core variable in measuring flagship model quality.

5. Practical Implications: What Enterprises and Developers Should Do

5.1 Model Selection: Look at Parameter Count, But Also at “Post-Training Water Level”

Models of the same parameter scale can deliver vastly different real-world experiences depending on post-training investment. Evaluation should focus on end-to-end success rates on real tasks, tool-calling reliability, and stability under long context—not just parameter count and exam scores.

5.2 Building In-House: A Four-Step Domain Post-Training Method

1. SFT as the foundation: Use high-quality domain samples to establish formatting and instruction baselines.
2. Build verifiers: Translate domain correctness into computable criteria (test sets, rule validation, simulation replay).
3. Small-scale RL to validate signals: First confirm on narrow tasks that reward signals are effective and loophole-free, then scale up.
4. Expand domains and concurrency: Gradually onboard more task domains and sandbox concurrency, monitoring KL and entropy metrics to prevent training collapse.

Common pitfalls include: reward functions being gamed (requires continuous auditing), unstable environments introducing noisy rewards, mid-context information being “diluted” in long contexts, and overly tight KL constraints causing insufficient exploration.

5.3 Leverage: Build on General Cloud Platform Capabilities

The infrastructure barrier for supporting hundreds of domains of parallel RL is extremely high. A more realistic path is to reuse general cloud capabilities: GPU compute and container platforms to carry training loads, object storage to manage massive trajectory data, message queues to decouple sampling from verification, and logging and monitoring to ensure long-running training stability. Cloud platforms such as Baidu AI Cloud also provide full-chain tools for model fine-tuning, evaluation, and deployment, allowing teams to focus on the highest-leverage links—task domain design and verifier construction.

6. Conclusion

Post-training is not a negation of pre-training but the last mile that cashes in the enormous potential accumulated by pre-training into real productivity. As competition in pre-training converges, the focus is shifting to “who has richer task domains, more precise verifiers, and higher scheduling efficiency.” It is foreseeable that as methodologies and toolchains mature, post-training capability will gradually diffuse from top labs to more enterprises and teams, becoming the “new basic skill” of the large model era.