Self-Compression of Chain-of-Thought via Multi-Agent Reinforcement Learning
Self-Compression of Chain-of-Thought via Multi-Agent Reinforcement Learning explores Optimize reasoning efficiency in AI models using multi-agent reinforcement learning to reduce inference overhead.. Commercial viability score: 6/10 in AI Optimization.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisTRiMS: Real-Time Tracking of Minimal Sufficient Length for Efficient Reasoning via RLViability: 5.0
- Builds On ThisMitigating Overthinking in Large Reasoning Models via Difficulty-aware Reinforcement LearningViability: 5.0
- Builds On ThisDoes Your Reasoning Model Implicitly Know When to Stop Thinking?Viability: 5.0
- Builds On ThisWhen and Why Does Unsupervised RL Succeed in Mathematical Reasoning? A Manifold Envelopment PerspectiveViability: 3.0
- Prior WorkTACLer: Tailored Curriculum Reinforcement Learning for Efficient Reasoning