In the 2026 domestic large model competition, a pioneering team achieved breakthroughs in intelligent programming models through an unconventional approach: maintaining a fixed 753B parameter scale while leveraging post-training scaling laws for capability enhancement. This controlled experiment validates post-training optimization effectiveness and establishes a new model evolution paradigm.

Technical Breakthroughs: Post-Training Scaling Paradigm Shift

Traditional model iterations follow “parameter expansion-compute stacking” patterns, while the new generation achieves capability jumps through three post-training dimensions while retaining the 753B MoE architecture (≈40B active parameters/token):

1. Extended Long-Range Environments

The training environment expanded 100x to include million-scale code repositories and ten-million-scale vulnerability databases. After one month of reinforcement learning:

• Terminal-Bench 3.0 score improved from 4.6 to 28.3
• Demonstrated precise control over complex terminal environments
• Environment complexity grew exponentially from single CLI interactions to full system operations including multi-process management, network configuration, and permission control

2. Automated Verification System

Innovative self-supervised Verifier generates dynamic test cases:

• DeepSWE v1.1 score increased by 20.7 points
• Real-time detection of logical flaws, performance bottlenecks, and security risks
• Example: Automatically generates boundary condition tests for recursive algorithms

3. Reinforcement Learning Infrastructure Optimization

The proprietary slime framework achieved three breakthroughs:

• Distributed policy gradient optimization supporting thousand-GPU clusters
• Dynamic reward shaping adjusting weights based on task complexity
• Prioritized experience replay enhancing sample utilization

These optimizations led to:

• 84.5% win rate in CyberGym cybersecurity tests
• 2x improvement in ExploitBench vulnerability exploitation
• Binary vulnerability POC generation reducing confirmation cycles from 72h to 8min

Engineering Validation: Controlled Experiment Metrics

The team proved technical path effectiveness through strict controls:

1. Frozen Base Model Strategy

Maintained GLM-5.2’s 78-layer Transformer architecture including:

• Mixture-of-Experts (MoE) routing
• Multi-head Latent Attention (MLA)
• Dynamic Sparse Activation (DSA)
• IndexShare mechanism

All capability improvements derived from post-training phases. Performance gains were 2.3x higher than parameter expansion approaches using identical base models.

2. Training Efficiency Comparison

Optimization Dimension	Traditional Parameter Expansion	Post-Training Scaling	Efficiency Gain
Hardware Cost	+300%	+45%	6.7x
Training Cycle	+200%	+15%	13.3x
Capability Improvement	120%	480%	4x

Application Scenarios: From Coding to Cyber Defense

Three core capabilities enabled by post-training optimization:

1. Intelligent Terminal Control

Industrial Control System (ICS) automation example:

# PLC Security Configuration Automation
def secure_plc_config(device_ip):
    with SecureShell(device_ip) as ssh:
        if not verify_credentials(ssh):
            raise SecurityError("Authentication failed")
        apply_security_baseline(ssh, BASELINE_V2)
        start_monitoring_thread(ssh)

2. Automated Software Engineering

CI/CD pipeline enhancements:

• Unit test generation (40% coverage increase)
• Intelligent code review (92% vulnerability detection)
• Performance bottleneck localization (35% response time reduction)

3. Proactive Cybersecurity Defense

Adaptive protection system flow:

graph TD
    A[Traffic Analysis] --> B{Anomaly Detection}
    B -->|Attack| C[Generate Protection Rules]
    B -->|Normal| D[Update Behavior Baseline]
    C --> E[Deploy to WAF/IDS]
    D --> A

Technical Implications: Post-Training Development Paradigm

1. Training Efficiency Revolution

Developers can rapidly adapt models to specific scenarios without lengthy pre-training. A cloud provider’s practice showed:

• 27% IOPS improvement in object storage optimization
• Training cost reduced to 18% of从头训练 (from-scratch training)

2. Model Lightweighting Trend

Controlling base model size reduces inference latency. After 8-bit quantization:

• 75% memory reduction
• 3.2x inference speed improvement

3. Continual Learning Mechanism

Dynamic update pipeline for ongoing data absorption:

# Continual Learning Framework
class ContinualLearner:
    def __init__(self, base_model):
        self.model = base_model
        self.replay_buffer = Deque(maxlen=10000)
    def update(self, new_data):
        self.replay_buffer.extend(new_data)
        train_loader = DataLoader(self.replay_buffer, batch_size=32)
        optimizer = AdamW(self.model.parameters(), lr=1e-5)
        for batch in train_loader:
            loss = compute_loss(batch)
            loss.backward()
            optimizer.step()

As model architecture innovations reach diminishing returns, post-training scaling opens new AI evolution dimensions. This “leveraging small forces” approach reduces R&D barriers while enabling rapid business adaptation. With technologies like automated verification and dynamic environment generation maturing, post-training optimization will become core to AI engineering, bridging the gap between lab experiments and production environments.