Self-improvement framework that enhances LLM agents through verbal feedback and reflection
Core Idea: Reflexion is a technique that improves LLM agent performance by implementing a feedback loop where agents reflect on their past actions, learn from failures, and integrate these reflections into future decision-making, enhancing their problem-solving capabilities without requiring model retraining.
Key Elements
Framework Components
- Actor: The LLM agent that chooses and executes actions based on observations
- Evaluator: Assesses the quality and success of the actor's outputs and decisions
- Self-Reflection: Generates insights about performance and strategies for improvement
- Memory Systems:
- Short-term memory: Tracks actions taken during current task
- Long-term memory: Stores reflections to guide future decision-making
Reflection Process
- Task Execution: Agent attempts to complete assigned task
- Performance Evaluation: Success or failure is determined
- Verbal Reflection: Upon failure, agent analyzes what went wrong
- Strategy Refinement: Agent formulates improved approaches
- Memory Integration: Reflections stored for future reference
- Subsequent Attempts: New actions informed by past reflections
Implementation Methods
- Prompted Reflection: Explicit prompts like "What went wrong? How can you improve?"
- Structured Templates: Standardized reflection formats focusing on specific aspects
- Meta-Prompting: The agent itself generates reflective questions
- Integration with ReAct: Combining with reasoning and acting framework for enhanced capabilities
Key Advantages
- Adaptive Learning: Improves without model weight updates or additional training
- Failure Recovery: Enables recovery from initial mistakes through iterative attempts
- Transparent Reasoning: Makes thought processes explicit and inspectable
- Generalization: Applies lessons from one task to similar future challenges
- Error Recognition: Develops ability to identify and correct reasoning flaws
Application Domains
- Complex Problem Solving: Math problems, puzzles, multi-step reasoning tasks
- Decision-Making: Games, strategy development, planning scenarios
- Code Generation: Debugging, optimization, and iterative improvement
- Research Tasks: Information gathering, hypothesis testing, and refinement
- Autonomous Agents: Long-running agents that improve over extended interactions
Connections
- Related Concepts: ReAct Framework (foundation technique), SELF-REFINE (similar self-improvement approach), LLM Agents (primary application)
- Broader Context: Reinforcement Learning from Human Feedback (similar goal, different method), Agentic AI Systems (application domain)
- Applications: Auto-debugging Systems, Research Agents, Game-playing Agents
- Components: Agent Memory Systems (enabling technology), Prompt Engineering (implementation method)
References
- Shinn, N., et al. (2023). "Reflexion: Language Agents with Verbal Reinforcement Learning." NeurIPS.
- Related work: Madaan, A., et al. (2023). "Self-refine: Iterative Refinement with Self-feedback." NeurIPS.
- Extension: Wang, K., et al. (2023). "Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents." arXiv.
#Reflexion #LLMAgents #SelfImprovement #VerbalReinforcement #ReasoningFrameworks #AgentSystems #IterativeLearning
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