Post-Training for Large Models and the Rise of Large-Scale Reinforcement Learning

Why Post-Training Has Become the New Competitive Focus

Over the past few years, major gains in large model capabilities have come primarily from continued investment in pre-training: larger parameter counts, better architectures, and larger volumes of higher-quality data. However, as the supply of high-quality public data slows and the marginal benefit of simply scaling up begins to decline, industry attention has naturally shifted to post-training.

The value of post-training lies in activating and stabilizing a model’s latent capabilities into deliverable task performance. After pre-training has taught the model general knowledge and language abilities, post-training uses repeated trial and error with reliable feedback to turn that foundation into measurable results. In the past one to two years especially, large-scale reinforcement learning has become a key method for improving reasoning and complex task performance. A notable trend is that leading teams are trying to extend capabilities accumulated in programming into more professional tasks that can be practically executed and repeatedly verified.

Two Main Paths for Improving Model Capabilities

From a model training engineering perspective, current capability improvements generally proceed along two directions.

1) Pre-training

• Scaling up: increasing parameter counts, expanding data scale, and investing more compute.
• Optimizing architecture: improving attention mechanisms, adopting mixture-of-experts structures, and strengthening long-context processing.
• Improving data quality: cleaning, deduplication, optimizing data mixes, and introducing high-quality synthetic data.

2) Post-training

• Supervised fine-tuning: using high-quality instruction data to align task formats and output styles.
• Preference alignment: using methods such as reinforcement learning from human feedback or direct preference optimization to make outputs better match human expectations.
• Large-scale reinforcement learning: optimizing policies for verifiable tasks such as math, coding, and tool use. This is currently the core path for improving reasoning and agent capabilities.

The relationship between the two can be summarized as: pre-training determines what the model knows, and post-training determines what the model can do well.

From “Right or Wrong” to “Run It”: The Evolution of Reward Signals

The key to understanding post-training is understanding how reward signals are obtained.

When training a model to solve math problems, judging correctness is straightforward once the model gives an answer. The reward signal is statically verifiable. But teaching a model to complete a complex programming task follows a completely different logic. The model needs a real execution environment where it can modify code, run tests, discover errors, and continue revising. The training system gives rewards and updates the model based on actual execution results. This process may require many rounds, and the reward is dynamic and execution-based.

A simplified reward function is shown below:

def compute_reward(task, trajectory):
    # 1. Outcome reward: whether the final code passes all tests
    result = sandbox.run(task.tests, trajectory.final_code)
    if not result.passed:
        return 0.0
    # 2. Process reward: whether intermediate steps are reasonable and efficient
    process_score = evaluate_steps(trajectory.steps)
    # 3. Efficiency constraint: penalize redundant trial-and-error and meaningless actions
    penalty = 0.01 * trajectory.redundant_actions
    return 1.0 + 0.2 * process_score - penalty

As shown above, reward design has evolved from a single right-or-wrong judgment to a multidimensional signal combining outcome, process, and efficiency. This reflects the growing technical depth of post-training.

Three Infrastructure Challenges for Large-Scale Agent RL

Training a model on thousands or tens of thousands of such tasks simultaneously presents engineering challenges far greater than the algorithm itself.

1) Sandbox Environments

A sandbox is the model’s training ground for trial and error. It must meet requirements for isolation, security, and reproducibility. A common approach is task-level isolation through containerization, combined with resource quotas to prevent abnormal code from destabilizing the host machine. Snapshot and rollback mechanisms ensure environment consistency across multiple sampling runs of the same task. General cloud capabilities such as container platforms, object storage, and logging services can serve as the underlying foundation for building this system.

2) Large-Scale Scheduling Systems

Training throughput often depends on the concurrency capacity of the environment pool. The system needs to efficiently match large amounts of GPU compute with large numbers of environment instances and manage their full lifecycle. A typical training loop looks like this:

for step in range(training_steps):
    batch = task_pool.sample(batch_size)      # Sample tasks
    trajs = policy.generate(batch)            # Model generates trajectories
    for t in trajs:                           # Execute in sandbox and collect feedback
        env = env_pool.acquire()
        t.reward = compute_reward(env.execute(t))
        env_pool.release(env)
    policy.update(trajs)                      # Update policy

A supporting monitoring and alerting system is also essential for detecting environment anomalies, task backlogs, and reward distribution drift.

3) Task Quality and Reward Reliability

Low-quality tasks can cause the model to learn incorrect signals. The most typical problem is reward hacking, where the model finds loopholes in the rules to score highly without actually completing the task. This requires building a rigorously validated task set and combining rule-based rewards with reward models to cross-check feedback reliability.

Verifiability Determines the Boundary of Capability Expansion

Programming has become the gold standard for reinforcement learning because it naturally has three characteristics: executable, verifiable, and decomposable. Code can run, test cases provide objective feedback, and complex tasks can be broken down into multiple rounds of interaction.

Following the same logic, this approach can extend to data analysis, operations troubleshooting, scientific computing, and other professional fields that require real execution and result feedback. The core criterion for judging whether a domain can benefit from large-scale post-training is simple: whether a high-quality environment can be built where the model can repeatedly try, fail, and receive reliable feedback.

Conclusion

The nature of post-training competition is shifting from algorithms to infrastructure. The contest is now about how many high-quality environments a model can use for repeated trial and error with reliable feedback. High-quality environments, reliable rewards, and large-scale scheduling together determine how much of the knowledge acquired during pre-training can be converted into real productivity. For developers and engineering teams, understanding this technical direction helps in evaluating where model capabilities come from and where their limits lie.