AdaptiveRL-CoT: Dynamic Length Control for Efficient Multi-Agent Reasoning
Abstract
A novel framework combining length-controlled reasoning with multi-agent collaboration for efficient problem-solving. The system dynamically adjusts reasoning depth and agent interaction based on task complexity, using reinforcement learning to optimize both computational efficiency and solution accuracy.
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Research Gap Analysis
Current approaches lack dynamic adaptation of reasoning length in multi-agent scenarios and don't effectively balance computational efficiency with collaborative problem-solving capability.
AdaptiveRL-CoT: Dynamic Length Control for Efficient Multi-Agent Reasoning
Motivation
Current reasoning models face two major challenges: the overthinking problem leading to computational inefficiency, and the inability to effectively collaborate with other agents for complex problem-solving. While recent work has shown progress in controlling reasoning length and improving single-agent performance, there's a critical need for systems that can dynamically adjust both reasoning depth and multi-agent collaboration patterns based on task complexity.
Proposed Approach
The framework consists of three key components:
1. Dynamic Length Controller
- Implements an adaptive policy that determines optimal reasoning length based on task characteristics
- Uses entropy-based metrics to identify critical decision points
- Incorporates feedback from solution success to adjust length parameters
2. Multi-Agent Coordinator
- Manages a pool of specialized reasoning agents
- Orchestrates agent interactions using learned collaboration patterns
- Implements token-level attention sharing between agents
3. Reinforcement Learning Optimization
- Joint optimization of length control and agent collaboration
- Custom reward function incorporating both efficiency and accuracy metrics
- Progressive training curriculum from simple to complex reasoning tasks
Expected Outcomes
- Reduced computational overhead through optimal reasoning length
- Improved problem-solving accuracy through strategic agent collaboration
- Emergent specialization among agents for different reasoning subtasks
- Quantifiable efficiency gains in terms of token usage and processing time
Potential Applications
- Complex mathematical problem solving
- Scientific research assistance
- Legal document analysis
- Medical diagnosis support
- Financial risk assessment
- Educational tutoring systems
Proposed Methodology
Implement a three-tier system combining length-controlled reasoning, multi-agent coordination, and reinforcement learning optimization with entropy-based token selection.
Potential Impact
Could significantly reduce computational costs while improving reasoning accuracy, enabling more efficient deployment of AI reasoning systems in resource-constrained environments and complex real-world applications.