Post-Training as the Key to Scaling Breakthroughs: How Ultra-Large Models Unlock Their Full Potential

From Scale to Effectiveness: Post-Training Becomes the Main Battleground for Model Capability

As large model parameter counts cross the trillion mark, the marginal returns from the pre-training phase are steadily shrinking. Pre-training equips models with massive knowledge representations and foundational language understanding, but these capabilities do not come in a directly usable form—they resemble raw ore, rich in potential yet unrefined. What truly determines a model’s practical value is the post-training phase that follows, which converts raw pre-trained abilities into precise, controllable, and deployable intelligent services through carefully designed data strategies, alignment mechanisms, and optimization objectives.

Industry observations consistently show that, at the same parameter scale, differences in post-training strategy can lead to performance gaps of 20%–40% on specific tasks. This explains why recent model releases have shifted emphasis from the brute-force approach of “bigger parameters + more data” toward the engineering discipline of refined post-training pipelines. Post-training is not a single fine-tuning step—it is a complex system composed of multiple stages, objectives, and strategies. Below, we analyze several core technical dimensions.

Dimension 1: Data Engineering—The Foundation of Post-Training Quality

High-Quality Data Selection and Ratio Strategies

The first step in post-training is data construction. Unlike pre-training, which pursues sheer volume, post-training imposes strict requirements on precision, diversity, and distributional balance. The core challenge is: how to identify high-quality samples from massive candidate datasets that effectively unlock the model’s latent capabilities?

In practice, a three-tier filtering mechanism is commonly adopted. The first tier is rule-based filtering, which removes low-quality content through deduplication algorithms, language identification, and text quality scoring. The second tier is model-assisted filtering, where a discriminator model scores candidates and retains high-scoring samples. The third tier is task-oriented ratio allocation, which determines the proportional mix of data types—code instructions, mathematical reasoning, conversational interaction, tool use, and so on—based on a target capability matrix. For example, enhancing code and Agent capabilities requires significantly increasing the proportion of real terminal operation traces and complex tool-call chains.

Synthetic Data: The Key to Unlocking Capabilities

When naturally distributed data cannot fully cover target capabilities, synthetic data becomes a critical supplement. The emerging industry consensus is that well-designed synthetic data can substantially boost logical reasoning and code generation, sometimes outperforming natural data of comparable scale.

The value of synthetic data lies not only in volume but in controllability. Developers can target weak spots to generate training samples on demand, forming a closed loop of “identify weakness → generate targeted data → train and improve → re-evaluate.” However, synthetic data also carries risks of homogenization and distribution drift. Diversity and difficulty control mechanisms must therefore be introduced to ensure generated data remains both challenging and faithful to real-world distributions.

Dimension 2: Reinforcement Learning—The Critical Leap from Imitation to Beyond

Process Reward Models and Step-Level Supervision

Traditional supervised fine-tuning requires models to mechanically replicate the format of reference answers. This remains at the level of pattern copying and does little to develop genuine reasoning and planning abilities. Recent progress combines reinforcement learning with process reward models (PRMs) that deliver fine-grained reward signals for every step of the model’s reasoning.

Unlike sparse reward methods that only score the final result, process rewards identify “which reasoning step is correct and where deviations occur.” This local, immediate feedback refines the optimization granularity from entire outputs to individual reasoning steps, greatly improving the efficiency of policy gradient updates. The advantage is especially pronounced in tasks with demanding reasoning chains, such as mathematical derivation and code generation.

Group Policy Optimization: The Cornerstone of Stable Training

When reinforcement learning is applied to large model training, controlling the variance of policy gradients is a core engineering challenge. Group policy optimization—a method that groups samples and computes relative advantages within each group—has become a mainstream choice. Its core idea is to sample multiple output paths from the current policy and use within-group comparisons to determine the quality of each action.

This relative comparison naturally eliminates the impact of environment noise and inconsistent reward scales, leading to more stable training. Meanwhile, introducing KL divergence constraints prevents the model from drifting too far from its original distribution, preserving a balance between output diversity and general-purpose capabilities.

Synergy of Verifiable and Generative Rewards

The current trend in reward signal design is to combine verifiable rewards with generative rewards. For scenarios with clear objective criteria—such as code execution results or mathematical answers—automated verification can provide precise, zero-bias, reproducible reward signals. However, many real-world tasks lack such verifiable interfaces, necessitating dedicated reward models trained to simulate human preferences.

A mature post-training framework configures different reward channels for different task types and dynamically adjusts their weights during training. Early stages are dominated by verifiable rewards to ensure the model quickly masters hard rules; later stages gradually introduce generative rewards to improve the naturalness and overall quality of outputs.

Dimension 3: Multimodal Alignment—Building a Unified Perception and Reasoning Space

Strategies for Vision-Language Joint Training

Native visual understanding means the model does not rely on external systems to convert images into text before processing. Instead, visual and linguistic features interact and fuse within the model itself. Building this native capability involves two key steps:

The first is modality alignment pre-training, which uses massive image-text pair datasets to map visual encoder outputs into the language model’s semantic space, establishing cross-modal correspondences. The second is joint instruction tuning, performing supervised fine-tuning on multi-turn dialogue data that includes image inputs so the model learns to reason and converse based on visual content.

An often-overlooked technical detail is that the number of visual tokens has a major impact on performance. High-resolution images generate large numbers of visual tokens, significantly increasing attention computation overhead and diluting the attention weight of text tokens. A pragmatic approach is dynamic resolution—using high-resolution tiling for document and chart-dense images while compressing token counts for natural scene images, striking the optimal balance between detail fidelity and computational efficiency.

Mitigating Cross-Modal Interference

Another core challenge in multimodal models is cross-modal information interference. Text embeddings and visual features occupy different regions of the semantic space, and naive concatenation can cause conflicting gradient directions. Solutions typically include independent projection layers and normalization parameters for each modality, as well as alternating freezing of certain modules during training to stabilize the optimization path. Evidence shows that well-designed modality fusion can boost average multimodal task performance by 8%–15% without degrading pure-text task performance.

Dimension 4: Inference Optimization—The Last Mile from Capability to Service

Speculative Sampling and Dynamic Batching

When model parameters reach the trillion level, inference latency directly determines product usability. A mainstream approach is speculative sampling—using a small draft model to generate several candidate tokens first, then having the large model verify multiple tokens in a single parallel pass. Because verification can process several or even over a dozen tokens at once, the total number of decoding steps drops sharply, improving throughput by 2× to 3× while producing outputs identical to standard autoregressive decoding.

At the same time, dynamic batching allocates compute resources in real time based on actual request arrival patterns, maximizing hardware utilization for parallelizable requests. Combined with speculative decoding, this delivers meaningful latency gains without sacrificing output quality.

Engineering Challenges of Long-Context Inference

Million-token context support imposes additional burdens on inference systems. Longer contexts mean larger key-value cache memory footprints and higher attention computation complexity. Engineering solutions typically employ sparse attention mechanisms, replacing full attention with a combination of local attention and global anchor-token attention. Paired with key-value cache quantization and tiered eviction strategies, these techniques achieve a practical trade-off among memory usage, compute load, and information retention.

Key Takeaways and Reflections on Post-Training

Three core lessons emerge from this technical evolution:

1. Data quality far outweighs data quantity. A precisely curated dataset of a few thousand samples can drive a more significant capability leap than a blindly expanded million-sample set.
2. Process supervision is more instructive than outcome supervision. Fine-grained step-level feedback teaches the model how to think, not just what answer to give.
3. Post-training is not only about making models smarter—it is about making them more controllable, more efficient, and better aligned with real deployment needs.

The competition among large models is entering deeper waters. Parameter scaling alone is no longer the sole decisive factor; the sophistication of post-training techniques will increasingly become the dividing line that separates model capabilities. Understanding and mastering the post-training pipeline is an essential discipline for any team that aims to build truly usable large-scale language models.