-->

⏱️ Estimated Reading Time: 15 minutes

Overview

Moonshot AI’s Kimi-Researcher is an autonomous research agent built through End-to-End agentic reinforcement learning. It performs an average of 23 reasoning steps and explores over 200 URLs, achieving a remarkable 26.9% performance on Humanity’s Last Exam (HLE).

This post provides an in-depth analysis of Kimi-Researcher’s innovative technical approaches and the new paradigm of End-to-End agentic reinforcement learning.

Kimi-Researcher Core Achievements

Benchmark Performance

Kimi-Researcher demonstrated outstanding results on Humanity’s Last Exam (HLE) with initial performance at 8.6%, final performance at 26.9% (Pass@1), Pass@4 accuracy at 40.17%, and performance improvement of 18.3 percentage points through pure End-to-End RL.

Excellent Performance Across Various Benchmarks

Kimi-Researcher achieved strong performance across multiple benchmarks. In HLE, it reached 26.9% pass rate at 1 and 40.17% at 4, representing an improvement of 18.3 percentage points. In xbench DeepSearch, it achieved 69.0% pass rate at 1, outperforming o3 with search tools. It also showed strong performance in multi-turn search tasks including FRAMES and Seal_0, as well as in factual information tasks like SimpleQA.

Agent Capability Metrics

The system demonstrates impressive capabilities with an average of 23 reasoning steps, exploration of over 200 URLs, maximum of over 70 search queries in a single trajectory, and context windows spanning hundreds of thousands of tokens.

Innovation in End-to-End Agentic Reinforcement Learning

Limitations of Existing Approaches

Traditional workflow-based systems face several limitations. Multi-agent workflows rely on manual rule-based coordination, are dependent on specific LLM versions, require manual updates for environmental changes, and have limited scalability and flexibility.

Imitation learning approaches also have significant problems. Supervised fine-tuning faces difficulties in labeling long trajectory data, lacks generalization in dynamic environments, and is vulnerable to tool version changes.

Innovation of End-to-End Agentic RL

End-to-End agentic RL represents a fundamental innovation in AI agent development. The Kimi-Researcher model integrates parallel search tools, text-based browsers, and code execution tools within a comprehensive context management system.

The forward process generates thinking and actions from state observations, where each state leads to both thinking and action generation. When the action is “finish,” the system terminates; otherwise, it executes tools and updates the state through the context manager.

The holistic learning approach involves exploring massive strategies, calculating rewards for correct solutions, and learning from entire trajectories. This approach naturally handles long on-policy reasoning, adapts to changing tools and environments, and integrates planning, perception, and tool usage learning.

Core Technical Architecture

Training Data Engineering

The system employs tool-centric task design that generates tasks impossible to solve without tools. These include real-time information retrieval tasks, complex web navigation requirements, and efficiency gains of up to ten times faster than manual approaches.

The system measures tool invocation rates across different task types to ensure effective tool usage during training. This approach forces models to develop genuine tool-using capabilities rather than relying on naive approaches.

Reasoning-Intensive Task Generation

The system generates math and code tasks alongside hard search tasks that require iterative search-synthesis-reasoning cycles. These tasks involve information integration under context constraints, multi-source searches, reliability assessments, logical consistency verification, and final conclusion derivation.

The automated pipeline generates question-answer pairs, validates answers, filters for quality, and extracts ground truth. It maintains only non-trivial questions through Pass@N filtering to ensure challenging and meaningful training data.

Reinforcement Learning Training System

The REINFORCE algorithm optimization employs strict on-policy data generation and negative sample control for stable RL training. The training process generates on-policy trajectories, controls negative samples to prevent entropy collapse, calculates outcome-based rewards, and updates policies accordingly.

On-policy data generation disables LLM engine format enforcers to use only model probability distributions. This ensures pure on-policy learning without external constraints that might bias the learning process.

Negative sample control strategically removes some negative samples to prevent entropy collapse while maintaining learning signal quality. This balance is crucial for stable training in complex agentic environments.

Context Management System

The context management system handles long trajectories by maintaining important information while removing unnecessary documents. When context length exceeds limits, the system selects important information based on importance scoring and combines it with new information.

The importance scorer evaluates segments based on relevance to the current task and historical importance. This allows the system to extend trajectory length by thirty percent, enabling more iterations and higher information gain, leading to significant performance improvements.

Emergence of New Agent Capabilities

Conflict Resolution Ability

One remarkable capability Kimi-Researcher demonstrated is resolving conflicting information from multiple sources. In classical text analysis cases, the system can identify discrepancies between different translations and versions, cross-verify sources, analyze version differences, confirm original text authority, and consider potential adaptations in translation processes.

The iterative hypothesis refinement process generates hypotheses for each information source, cross-verifies each hypothesis, refines based on verification results, and synthesizes final conclusions from all refined hypotheses.

Rigorous Verification Capability

The system demonstrates careful verification abilities even for seemingly straightforward questions. In complex query scenarios, it performs initial reasoning, conducts additional searches for verification, performs multilingual cross-verification, confirms information from official and authoritative sources, and provides comprehensive verification before final answers.

The deliberate additional search approach treats even apparently simple questions with caution, conducts intentional searches for verification, and cross-validates initial answers with additional sources.

Practical Application Cases

Academic Research Support

The system provides automated literature reviews through related paper discovery, citation analysis, trend identification, and research gap identification. For hypothesis generation, it offers pattern recognition in existing research, discovery of novel connections, and proposals for testable hypotheses.

For regulatory compliance checks, the system identifies applicable laws, analyzes compliance requirements, assesses regulatory risks, and proposes mitigation strategies. In case law analysis, it searches for relevant precedents, analyzes legal reasoning processes, and predicts outcomes.

Clinical Evidence Review

The system conducts systematic reviews through study identification, quality assessment, evidence synthesis, and clinical recommendations. For drug interaction analysis, it detects interactions, assesses severity, and suggests alternatives.

Significance of Technical Innovation

Paradigm Shift in AI Agent Development

The research represents a fundamental shift from traditional workflow approaches with manual rule-based coordination and limited scalability, and imitation learning with labeling difficulties and poor generalization, to end-to-end RL with holistic problem-solving learning and natural handling of long trajectories.

Future implications include significant advancement in agent intelligence, reduced manual intervention in development efficiency, dynamic environment handling for adaptability, and readiness for large-scale deployment.

Innovation in Research Methodology

The automated research pipeline encompasses question formulation, literature search automation, evidence synthesis automation, hypothesis testing support, and result interpretation assistance. Quality assurance mechanisms include multi-source verification, bias detection, reproducibility assurance, and peer review support.

Future Development Directions

Evolution to General-Purpose Agents

The capability expansion plan focuses on current strengths in search and reasoning while expanding to creative content generation, complex problem solving, multi-domain expertise, and real-time collaboration with an ever-expanding toolkit.

Infrastructure advancement includes enhanced RL algorithms for training stability, optimized training pipelines for efficiency improvements, larger scale deployment for scalability, and production-ready systems for reliability.

Open Source Contributions

Planned releases include base pretrained models, RL trained models, training infrastructure, evaluation benchmarks, with a timeline spanning the following months. This will facilitate democratized access to advanced AI, reproducible research results, faster research progress, and collaborative development in the community.

Conclusion

Moonshot AI’s Kimi-Researcher presents a new paradigm in End-to-End agentic reinforcement learning, bringing revolutionary changes to AI agent development.

Key Achievement Summary

The research achieved performance innovation with an 18.3 percentage point improvement from 8.6% to 26.9% on HLE, technical innovation through pure End-to-End RL for agent capability development, capability emergence including conflict resolution and rigorous verification, and practical value with immediate applicability to various research and analysis tasks.

Future Prospects

The system shows potential for expansion from search-reasoning to general problem solving, open source contributions of models and infrastructure to the research community, continuous improvement in End-to-End agentic RL methodology, and accelerated practical applications across various domains.

Kimi-Researcher goes beyond being simply a high-performing AI model to demonstrate the possibility that AI agents can perform complex research and reasoning like humans. This opens new horizons in AI research and will serve as the foundation for developing more intelligent and autonomous AI systems in the future.

참고