LLMs Can Google Themselves: Self-Search RL Boosts Question Answering

Overview

Paper Summary › Explain Like I'm Five › Conflicts of Interest › Identified Limitations › Rating Explanation › Good to know › Topic Hierarchy › File Information ›

Paper Summary

Paperzilla title

This research shows that large language models can effectively answer questions by searching their internal knowledge. A new technique called Self-Search Reinforcement Learning (SSRL) improves this ability, surpassing the performance of methods that rely on external search engines like Google. However, efficiently extracting the single best answer from multiple internally generated samples remains a challenge.

Explain Like I'm Five

Big language models can answer questions by searching their own internal knowledge base. This "self-search" can be improved with reinforcement learning to boost performance and reduce the need for costly external searches like Google.

Possible Conflicts of Interest

None identified.

Identified Limitations

Limited benchmark scope

The benchmark selection is limited, primarily focusing on question-answering tasks and lacking diversity in other application areas. This raises concerns about the generalizability of the findings to broader NLP tasks.

Insufficient analysis of knowledge vs. reasoning

The paper acknowledges the need for further investigation into knowledge utilization vs. reasoning but doesn't delve deep into this aspect. A more detailed analysis would strengthen the conclusions.

Ineffective majority voting

The majority voting approach for consolidating multiple samples proved ineffective, highlighting the challenge of extracting the best answer from the model's internal knowledge.

Inconsistency with prior findings

The comparison between Qwen and LLaMA models reveals inconsistencies with prior findings in reasoning tasks, suggesting a need for more research to clarify the relationship between self-search ability and reasoning priors.

Lack of analysis on computational cost

The paper doesn't discuss the computational cost of repeated sampling, which could be a limiting factor for large models and datasets.

Rating Explanation

This paper presents a novel approach to improving LLM question-answering by leveraging their internal knowledge. The methodology is sound, the results are promising, and the analysis provides valuable insights into the potential of LLMs as world models. However, the limited benchmark scope and insufficient exploration of certain aspects prevent a perfect score.

Good to know

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Topic Hierarchy

Domain: Physical Sciences

Field: Computer Science

Subfield: Artificial Intelligence

File Information

Original Title: SSRL: SELF-SEARCH REINFORCEMENT LEARNING

Uploaded: August 20, 2025 at 08:19 PM

Privacy: Public