Post-Training for Very Large Models and Improving Long-Horizon Agent Capabilities

1. The Core Logic Behind Upgrading Very Large Models

Today, improving very large language models is no longer simply about increasing parameter counts. What matters more is how much compute, data coverage, and reinforcement learning task diversity are invested during post-training. These factors are becoming the key determinants of a model’s upper performance limit.

Industry practice shows that new-generation models usually advance along three axes:

1. Increasing post-training compute: More resources are allocated to preference alignment, complex reasoning, tool use, and long-horizon task training rather than being spent only on pretraining.
2. Expanding reinforcement learning coverage: Training is extended from early single-task formats such as question answering and summarization to hundreds of downstream domains, including code generation, terminal operations, financial analysis, and 3D tool invocation.
3. Rebuilding training and execution infrastructure: This includes reinforcement learning frameworks, long-context inference engines, sandbox environments, task scheduling systems, and environment quality inspection platforms.

The core objective of these changes is to make models not only able to answer questions, but also capable of continuously completing complex work in real environments.

2. Engineering Challenges in Scaling Reinforcement Learning

Scaling reinforcement learning to hundreds of downstream domains is not simply a matter of adding more training tasks. It requires the training system to handle several issues at the same time.

2.1 Stability of Reward Signals

Different domains define rewards very differently. Code tasks may use test pass rates as the reward, terminal operations may use whether the task executed successfully, and financial research may use the accuracy and completeness of analytical conclusions.

As a result, the training framework needs to support multi-reward fusion, reward normalization, and dynamic weight adjustment. Otherwise, the model can easily over-optimize for one category of tasks while degrading on others.

2.2 Memory and Compute Pressure from Long-Context Reasoning

Training samples for long-horizon tasks typically include multiple rounds of tool calls, environment feedback, and intermediate states. These samples can reach hundreds of thousands or even millions of tokens in context length.

Engineering solutions such as blockwise attention, state compression, gradient checkpointing, and sequence parallelism are needed to complete training within a manageable cost envelope.

2.3 Environment Consistency and Reproducibility

Reinforcement learning depends heavily on environment feedback. If sandbox environments have version differences, dependency conflicts, or state contamination across training tasks, reward signals become distorted.

A common industry practice is therefore to build standardized container images, read-only dependency layers, and resettable sandbox states to ensure consistent execution conditions for every task.

3. Evaluating and Improving Long-Horizon Agent Capabilities

Unlike pure conversation, long-horizon agents need capabilities such as goal decomposition, multi-step execution, tool selection, error recovery, and result verification.

3.1 Challenges in Evaluating Long-Horizon Tasks

Traditional benchmarks usually measure correctness in single-turn or multi-turn question answering. Long-horizon tasks require a model to complete a full loop from planning to delivery in a resource-constrained environment.

For example, in terminal benchmarks, a model needs to:

• Understand the task description;
• Inspect the current environment state;
• Select appropriate commands;
• Handle error output;
• Pass predefined tests at the end.

The core value of such evaluation is that it measures comprehensive performance along a real execution chain, rather than text generation quality alone.

3.2 Main Approaches to Improving Long-Horizon Capabilities

Increase trajectory training samples: Let models learn complete task execution trajectories, including success paths and failure recovery paths.

Introduce process supervision: Reward not only the final result, but also supervise the reasonableness of intermediate steps.

Increase tool-call diversity: Cover more tool types during training, such as code execution, file operations, network requests, and database queries.

Strengthen error recovery: Deliberately inject abnormal states into training environments so that models learn to identify and fix problems.

4. Balancing Code Capability Improvement and Inference Acceleration

Code generation and coding agents are currently among the most competitive areas of model capability development.

4.1 Special Requirements of Code Scenarios

Code tasks require models to have:

• Strict syntax constraints;
• Understanding of project structure;
• Multi-file collaborative editing ability;
• Test-driven problem verification;
• Accurate interpretation of terminal output and error messages.

Code models therefore need extensive exposure during training to real repository structures, build scripts, testing frameworks, and terminal interaction logs.

4.2 High-Speed Inference While Preserving Intelligence

As model capabilities improve, inference speed often becomes the main bottleneck in real-world deployment. This is especially true in agent scenarios, where a single task may involve dozens of model calls. Any latency in a single call is amplified across multiple rounds of execution.

A common industry approach is to keep model weights and intelligence essentially unchanged while improving output speed through the following methods:

• Introducing speculative sampling with a small draft model to predict candidate tokens;
• Optimizing KV cache management to reduce redundant computation;
• Applying operator fusion and memory optimization for long-sequence inference;
• Deploying dedicated inference clusters to reduce queueing delays.

These measures can increase output speed several times without significantly lowering task success rates.

5. Managed Agents and Long-Running Cloud Execution

Beyond offering direct model invocation APIs, more platforms are beginning to provide long-running agent execution capability as a managed service.

5.1 Why Managed Agents Are Needed

A complete agent task often cannot be finished with a single API call. It may last for hours or even days and requires:

• Maintaining execution state over a long period;
• Preserving context across multiple tool-call rounds;
• Supporting parallel or collaborative execution of multiple agents;
• Handling exceptions, timeouts, and retries during execution.

If developers build these capabilities themselves, they need to create additional modules for task orchestration, state persistence, error recovery, and monitoring and alerting. Managed agent services move these responsibilities down into cloud infrastructure.

5.2 Key Components of a Managed Agent Platform

A typical managed agent platform usually includes:

Task queue and scheduler: Responsible for receiving tasks, allocating execution resources, and controlling concurrency.

Execution sandbox: Provides an isolated runtime environment supporting code execution, file access, and tool invocation.

State storage: Stores intermediate states and context during task execution.

Multi-agent communication mechanism: Enables agents to pass messages, share results, and coordinate actions.

Monitoring and logging: Records every tool call, output, and error message for debugging and auditing.

With this type of managed capability, developers can focus more on business logic design rather than building and maintaining the underlying execution infrastructure.

6. Cost Structure and Practical Usage Considerations

API pricing for very large models is typically calculated separately for cached input tokens, uncached input tokens, and output tokens. This model creates new cost-optimization requirements for developers.

6.1 Cache Hit Rate Directly Affects Cost

During multi-round agent execution, system prompts and tool descriptions usually remain unchanged. Platforms therefore provide prefix caching to reuse repeated context.

If developers design prompt structures carefully and place fixed content in the prefix, they can significantly improve cache hit rates and reduce input costs.

6.2 Tradeoffs Between High-Speed and Standard Versions

High-speed versions usually provide faster output at the same intelligence level, but cost more. For interactive applications such as code completion and real-time conversation, the experience improvement from lower latency may justify the additional cost. For batch tasks and offline analysis, the standard version may be more economical.

Developers are advised to configure different modules differently based on how latency-sensitive each task is.

7. Practical Advice for Developers

Based on the analysis above, teams that need to deploy very large models and long-horizon agent capabilities in their own business can start from the following areas.

7.1 Build an Observable Execution Chain First

Before integrating model and agent capabilities, set up a complete logging, metrics, and tracing system. Every tool call and every model output should be recorded. Otherwise, when a long-horizon task fails, it is difficult to identify which stage caused the problem.

7.2 Start in a Closed Environment

Do not run unverified agents directly on open networks in the early stages. Restrict execution to internal sandboxes or test environments first, gradually verify task success rates and error patterns, and then expand execution boundaries.

7.3 Break Tasks into Verifiable Subgoals

Success rates for long-horizon tasks are often much lower than for single-step tasks. When designing agent workflows, break large goals into multiple independently verifiable subgoals as much as possible. Even if the overall task fails, intermediate results can be preserved and repeated execution costs reduced.

7.4 Focus on Post-Training Capability Rather Than a Single Benchmark

Public benchmark scores have some reference value, but there is usually a clear gap between real business scenarios and benchmarks. Developers are advised to build internal evaluation sets based on their own tasks and continuously assess different models and configurations through regression testing.

8. Summary

Improvements in very large language models have entered a stage where post-training scaling and long-horizon execution capability are equally important. Reinforcement learning domain coverage, training infrastructure stability, code and terminal execution ability, inference acceleration, and managed agent services together form the key foundation for deploying next-generation models on complex tasks.

For developers and enterprises, rationally evaluating model performance in real business pipelines and building observable, recoverable, and scalable execution systems is more valuable than focusing solely on parameter counts or leaderboard scores. As model weights become more openly available and managed services become more widespread, long-horizon agents will gradually move from technical validation into production environments.