Agent Training and Fine-Tuning Techniques

Agent Training and Fine-Tuning Techniques #

Effective training transforms basic language models into capable agents. Understanding training techniques is essential for building reliable systems.

Training Paradigms #

• Supervised learning: Learning from labeled examples
• Reinforcement learning: Optimization through reward signals
• Imitation learning: Mimicking expert behavior
• Self-play: Agents learning by competing or collaborating

Fine-Tuning Strategies #

• Full fine-tuning: Update all model parameters
• Parameter-efficient: LoRA, adapters for limited changes
• Prompt tuning: Optimize input representations
• Continual learning: Incremental updates without forgetting

Evaluation Methods #

• Benchmark suites: Standardized task evaluation
• Human evaluation: Quality assessment by experts
• Automated metrics: Speed, accuracy, cost efficiency
• Stress testing: Performance under extreme conditions