SynPlanResearch-R1: Encouraging Tool Exploration for Deep Research with Synthetic Plans
SynPlanResearch-R1: Encouraging Tool Exploration for Deep Research with Synthetic Plans explores SynPlanResearch-R1 improves research agent performance by synthesizing tool-use trajectories for better exploration, offering a strong initialization for reinforcement learning.. Commercial viability score: 7/10 in Agents.
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 $10K - $14K 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
- Prior WorkAutoResearch-RL: Perpetual Self-Evaluating Reinforcement Learning Agents for Autonomous Neural Architecture DiscoveryViability: 7.0
- Prior WorkMarco DeepResearch: Unlocking Efficient Deep Research Agents via Verification-Centric DesignViability: 7.0
- Competing ApproachFrom Self-Evolving Synthetic Data to Verifiable-Reward RL: Post-Training Multi-turn Interactive Tool-Using AgentsViability: 3.0
- Competing ApproachAnticipatory Planning for Multimodal AI AgentsViability: 7.0
- Competing Approach
Related Resources
- AgentSpeak(glossary)
- Mixture-of-Agents(glossary)
- Agents(glossary)
- What is the future of AI agents according to Nothing's CEO?(question)
- How do LLM efficiency advancements impact the development of AI agents?(question)
- How does AgentXRay contribute to the explainability of AI agents in complex decision-making processes?(question)
- Agents – Use Cases(use_case)