Advanced Features

This section is designed for power users, covering deep monitoring, distributed scaling, and inference optimization within the BEAM framework.

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Training Monitors

BEAM includes built-in hooks to synchronize training metrics with third-party platforms in real-time.

• Metric Tracking: Log Reward, Edge Probabilities, and Token Usage per round.
• Visualization Integrations:
  • WandB / TensorBoard: Use WandBLogger to automatically generate topology evolution curves.
  • In-built Profiler: Use EdgeTracker to export heatmaps of the adjacency matrix evolution.

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Distributed Evolution

For large-scale agent networks or high-throughput tasks, BEAM supports distributed architectures.

• Parallel Strategies:
  • Task-Parallel: Execute different task samples across multiple nodes to accelerate Policy Gradient convergence.
  • Agent-Split: Partition complex, massive agent topologies across multiple GPUs.
• Sync Mechanism: Supports Asynchronous SGD to maintain high structural evolution efficiency even in high-latency network environments.

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Quantization & Inference Efficiency

To reduce production costs, BEAM provides optimization schemes for the logical topology.

Technique	Description	Benefit
Logit Quantization	Quantizes connection logits from FP32 to INT8.	Reductions in topology parameter storage by ~75%.
Mask Pruning	Physically cuts connections with weights below a threshold (Hard Pruning).	Significantly reduces Context Window pressure.
Static Compilation	Compiles the optimized DAG into a static execution engine.	Eliminates Python-layer scheduling overhead.

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Custom Optimizer Tuning

Fine-tune the learning behavior of the AgentPrune strategy:

from beam import OptimizationConfig

config = OptimizationConfig(
    strategy="prune",
    lr=1e-3,                    # Learning rate for topology weights
    entropy_coeff=0.01,         # Prevents premature convergence to local optima
    grad_norm_clip=1.0,         # Prevents gradient explosion
    warmup_steps=50             # Collect trajectories before triggering pruning
)

Heterogeneous Orchestration

Mix models of different scales (e.g., GPT-4o and Llama-3) within the same topology to balance reasoning power and cost.

# Use a high-reasoning model for coding, and a lightweight model for filtering
coder = Agent(name="Coder", llm=Registry.get("gpt-4o"))
filter_agent = Agent(name="Filter", llm=Registry.get("llama-3-8b", base_url="..."))

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