1. Paradigm Shift: From Pre-Training to Post-Training Scaling

In recent years, the dominant narrative for improving LLM capabilities was “pre-training scaling”: more data, more compute, and larger parameter counts. However, as high-quality corpora dwindle and the marginal returns on compute decrease, the industry’s focus is shifting toward post-training, especially the reinforcement learning (RL) phase.

This shift occurs because pre-training and post-training solve fundamentally different problems. Pre-training allows models to “know”—it compresses human knowledge, providing a foundation of language, code, and common sense. RL post-training teaches models to “do”—it places the model in interactive, verifiable environments, using task completion as a signal to repeatedly hone reasoning, planning, and tool-use capabilities. A helpful analogy: pre-training is like reading all the textbooks, while post-training is like continuous code reviews and retrospectives on real-world projects.

Recent models demonstrating significant capability leaps almost universally share the trait of substantially increased post-training compute. Supporting this transition requires not just algorithms, but a comprehensive engineering system, which is the focus of this article.

2. Why RL Post-Training Works: Verifiable Rewards and Multi-Domain Joint Training

1. From Imitation to Optimization: SFT vs. RL

Supervised Fine-Tuning (SFT) teaches models to imitate expert trajectories, where the upper bound is limited by the quality of the training data. Reinforcement Learning, conversely, optimizes the policy directly based on outcomes—whether code passes tests, mathematical reasoning is correct, or an agent task is completed within resource limits. For agentic tasks requiring “multi-step execution and mid-process trial and error,” RL’s advantages are particularly pronounced: the model can explore multiple paths in the environment, increasing the probability of successful paths and decreasing that of failed ones.

A simplified RL post-training loop looks roughly like this:

# Simplified RL post-training loop (illustrative)
for batch in task_dataloader:
    tasks = env_pool.sample(batch)             # Sample tasks from the environment pool
    traj = policy.rollout(tasks)               # Multi-turn interaction between model and environment
    reward = verifier.score(traj)              # Verifiable reward: test passed / compiled successfully / task achieved
    adv = compute_advantage(reward)            # Compute advantage to reduce variance
    policy.update(traj, adv)                   # Policy gradient update
    env_pool.gc_and_refresh()                  # Clean up low-quality environments, inject new tasks

2. Joint Training Across Hundreds of Domains: Preventing Collapse

When RL expands to cover hundreds of downstream domains—such as code generation, financial research, 3D creation, and office automation—the challenge shifts from “can it be trained” to “can it be trained stably.” Different domains have inconsistent reward scales, leading to mutual interference; reward hacking in certain tasks can pollute the overall policy; and long-sequence rollouts cause the compute ratio between training and inference to drift continuously.

The standard industry practice is to establish independent reward calibration and quality audit pipelines for each domain, while conducting joint training within a unified framework. This ensures that every increment of compute translates into stable returns rather than being consumed by environmental noise.

3. The Engineering Stack for Large-Scale RL: Six Essential Layers

“Optimizing every layer of the tech stack” translates to an engineering checklist that any team aiming for large-scale RL post-training cannot bypass:

1. RL Training Framework. The bulk of RL compute goes into rollouts (sampling). Training and sampling must collaborate efficiently. A common architecture decouples the inference engine from the training engine, using weight synchronization mechanisms for asynchronous updates, keeping GPU utilization high between “generating trajectories” and “updating parameters.”

2. Long-Context Inference Optimization. Million-token contexts mean the KV Cache alone can fill a single GPU’s memory. Sequence parallelism, paged memory management, KV Cache quantization and reuse, and attention operator fusion are standard techniques to transition long-context from “capable of running” to “cost-effective to run.”

3. Training Efficiency. Combinatorial strategies for data parallelism, tensor parallelism, and pipeline parallelism—paired with gradient accumulation, recomputation, and communication-computation overlap—determine the batch size and sequence length achievable with a given number of GPUs.

4. Sandbox Infrastructure. Rewards for agentic tasks come from “real execution.” Code generated by the model must run in an isolated environment: file system snapshots, network isolation, hard CPU/memory/time quotas, and result retrieval and validation. The throughput and stability of the sandbox directly dictate the production speed of RL data.

# Illustrative implementation of sandbox reward verification
def verify(patch: str, tests: list[str]) -> float:
    res = sandbox.run(
        patch, tests,
        timeout=600,        # Hard time limit to prevent infinite loops
        mem="4g",           # Resource quota simulating a constrained environment
        network=False,      # Network isolation for reproducibility
    )
    if res.exit_code != 0:
        return 0.0
    return res.passed / len(tests)   # Partial pass yields partial reward

1. Scheduling System. Heterogeneous workloads such as rollout, training, evaluation, and environment building run simultaneously on RL clusters. The scheduler must orchestrate tasks by priority and affinity, preventing the sampling side from waiting on the training side, or evaluation tasks from squeezing out training compute.

2. Environment Quality Inspection Platform. The upper limit of RL is determined by environment quality. Tasks with incorrect answers, unverifiable rewards, or ambiguous descriptions will push the model in the wrong direction. A shared quality inspection platform that performs automated checks for solvability, verifiability, and difficulty distribution is a prerequisite for scaling multi-domain joint training.

These six layers collectively address a “compute conversion rate” problem: how much of the newly added RL compute actually transforms into model capabilities. Competition among top teams is essentially a competition over this conversion rate.

4. Coding Agent Capabilities: Evaluation Demands More Rigor Than Training

1. Two High-Value Benchmark Paradigms

Current evaluations of coding agents are converging on two paradigms:

• Real Terminal Environment Testing: The model is placed in a resource-constrained, real-world terminal and codebase environment. Each task comes with independent, specialized tests, evaluating the ability to “work in messy environments”—reading code, installing dependencies, executing commands, fixing bugs, and passing tests. These tests correlate far better with daily development scenarios than traditional “function completion” benchmarks.
• Long-Horizon Engineering Benchmarks: Covering multiple engineering domains, these evaluate multi-step execution, tool calling, and end-to-end problem solving—from understanding requirements and designing solutions, to cross-file modifications and self-test repairs. They measure the capability to “stay on track during long-horizon tasks,” which is exactly what RL post-training targets for improvement.

2. Three Pitfalls in Evaluation Methodology

First, data contamination: if benchmark questions leak into pre-training corpora, scores will be systematically inflated. Regular question rotation and dynamic environments are necessary. Second, variance: agent task results are affected by sampling temperature and tool randomness; a single run is unreliable. Reports should include means and confidence intervals across multiple runs. Third, the disconnect between “benchmark scores” and “experience scores”: benchmarks evaluate closed tasks, while real-world usage involves code style, maintainability, and instruction fidelity. Teams should build private evaluation sets based on their own workflows, incorporating tasks like “fixing a real issue” or “building a project from scratch” into daily regression testing.

5. Long Context and Agents: Greater Than the Sum of Their Parts

The significance of million-token contexts for agents is not simply “reading more text,” but transforming how tasks are organized:

• Entire Codebase as Context: Cross-repository refactoring and dependency chain tracing no longer rely on the luck of multi-turn retrieval. The model can directly locate modification areas with a global perspective.
• State Continuity in Long-Horizon Tasks: After an agent executes dozens of tool calls, early constraints and discoveries can still be recalled, reducing the drift where the agent “forgets its initial goal midway.”
• Multi-Source Information Fusion: Scenarios like financial research and due diligence require accommodating hundreds of pages of documents alongside structured data. Long context allows “read, cross-validate, and draft” to be completed within a single session.

Engineering-wise, this combination demands specific features from inference services: prefix caching ensures system prompts and codebase indices are computed only once; streaming output and chunked prefilling improve first-token latency; and the structured injection of tool call results dictates the actual utilization rate of the context window.

6. Constant Intelligence, Multiple Speeds: The Second Frontier of Inference

Post-training enhances “intelligence,” while inference optimization enhances “affordability.” To multiply output speed several times while maintaining the same intelligence level, mainstream industry methods include speculative decoding (drafting by a small model, verified by the large model), fusion optimization for attention and sampling operators, KV Cache quantization with hierarchical caching, and continuous batching scheduling.

For developers, the value of a “high-speed version” lies in the fact that the end-to-end duration of multi-step agentic tasks roughly equals “single-step inference time × number of steps.” Speeding up a single step by several multiples means the deployment cost of long-horizon tasks drops proportionally, which is why inference optimization is elevated to a strategic height in the Agent era.

7. Developer Deployment Advice: From Evaluation to Engineering

Considering the technical landscape above, here are several actionable recommendations:

1. Build Your Own Evaluation Set. Extract 50 to 100 real tasks from business logs (including inputs, expected behaviors, and acceptance criteria) and solidify them as a regression evaluation. Rely on this when selecting models, rather than just looking at public leaderboards.
2. Design Tools and Context for Agents. Tool interfaces should be idempotent and return structured results; summarize and index long documents and codebases before injection; write key constraints into system prompts and periodically restate them to combat long-horizon forgetting.
3. Leverage Cloud Infrastructure. Whether building RL training or deploying inference services, general cloud capabilities like GPU clusters, container services, object storage, logging, and monitoring are standard. Platforms like Baidu AI Cloud provide a complete combination of these categories, allowing you to focus your energy on reward design and environment construction—the true differentiators.
4. Focus on Failure Recovery. Long-horizon tasks will inevitably fail. Sandbox snapshots, breakpoint recovery, and automatic attribution and retry of failed tasks determine the lower bound of an agent system’s availability.

8. Conclusion

In the post-training era, the upper limit of model capabilities is determined by algorithms, but whether those capabilities can be realized is determined by engineering. The RL framework, long-context inference, sandboxing, scheduling, and environment quality inspection—each layer of seemingly dry infrastructure dictates the conversion rate from “added compute” to “added capability.” The only way to discover the limits of the possible is to go a small step further into the impossible; and what makes this step steady and fast is the systemic advancement of the entire engineering stack. For developers, understanding this link is of far greater long-term value than memorizing any leaderboard score.