Yuqi-Zhou/LRAT-multilingual-e5-large

LRAT-E5

LRAT-E5 is a dense retriever obtained by fine-tuning intfloat/multilingual-e5-large-instruct with LRAT (Learning to Retrieve from Agent Trajectories), a training framework that adapts retrieval models to the behavior of search agents.

The central idea of LRAT is simple: if search is increasingly consumed by agents rather than humans, retrieval models should be trained from agent interaction data rather than only from human-centric supervision. This checkpoint is therefore optimized using query-document supervision mined from deep research trajectories.

What This Model Is For

This checkpoint is intended for:

• dense retrieval in agentic search and deep research systems
• retrieval for long-horizon information-seeking tasks
• evidence retrieval for multi-step reasoning pipelines
• replacing a general-purpose E5 retriever with one aligned to search-agent behavior

It is less suitable for:

• generic sentence embedding leaderboards without task-specific evaluation
• non-retrieval classification workflows
• fully unrelated domains without adaptation

Model Details

• Base model: intfloat/multilingual-e5-large-instruct
• Training framework: LRAT
• Retriever type: dense bi-encoder
• Objective: weighted contrastive learning with trajectory-derived supervision
• Intended use: retrieval for search agents operating in multi-step reasoning environments

Training Data

The trajectory-derived supervision used for LRAT is constructed from:

• 10K seed queries from InfoSeekQA
• Tongyi-DeepResearch-30B-A3B as the trajectory generation agent
• Wiki-25-Dump as the retrieval corpus
• multiple retrievers during collection, including BM25 and Qwen3-based retrievers

The paper reports:

• 26,482 valid trajectories
• 91,713 training pairs

For more details, please refer to the Training Data.

Training Configuration

The paper reports the following fine-tuning setup:

• epochs: 2
• batch size: 32
• learning rate: 1e-6
• maximum sequence length: 512
• group size: 10
• temperature: 0.02

The training is implemented with a FlagEmbedding-based dense retriever recipe.

Evaluation Summary

LRAT-E5 is evaluated inside six different search agents on:

• InfoSeek-Eval
• BrowseComp-Plus

The gains are consistent across both smaller task-optimized agents and larger generalist agentic foundation models.

Representative Results for This Backbone

Agent Backbone	InfoSeek-Eval SR	BrowseComp-Plus SR	BrowseComp-Plus Recall
AgentCPM-Explore (base)	47.3	15.9	26.5
AgentCPM-Explore (+ LRAT)	49.7	15.9	32.1
WebExplore (base)	60.0	25.4	50.4
WebExplore (+ LRAT)	63.3	29.0	56.1
Tongyi-DeepResearch (base)	56.7	20.7	54.8
Tongyi-DeepResearch (+ LRAT)	68.0	23.9	61.8
GPT-OSS (120B, base)	41.7	10.8	50.1
GPT-OSS (120B, + LRAT)	50.7	13.1	56.0
MiniMax-M2.1 (base)	64.0	46.4	64.9
MiniMax-M2.1 (+ LRAT)	75.0	48.7	69.7
GLM-4.7 (base)	73.7	46.4	68.7
GLM-4.7 (+ LRAT)	81.7	50.6	76.3

Across these agents, LRAT improves:

• InfoSeek-Eval success rate by roughly +5.1% to +21.6% relative
• BrowseComp-Plus success rate by roughly +0.0% to +21.3% relative
• BrowseComp-Plus evidence recall by roughly +7.4% to +21.1% relative

Usage

This model should be used with the same preprocessing and embedding extraction recipe as the upstream E5 backbone and the LRAT codebase.

from transformers import AutoTokenizer, AutoModel

model_id = "Yuqi-Zhou/LRAT-multilingual-e5-large"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModel.from_pretrained(model_id, trust_remote_code=True)

# Apply the same pooling / normalization setup used for
# multilingual-e5-large-instruct in your retrieval pipeline.

If you use E5-style query instructions or prefixes in your system, keep them consistent with your evaluation and indexing setup.

License and Release Notes

Please confirm the final release license against:

• the upstream base model license
• the released training dataset license
• any additional source corpus obligations

This card can be updated later with a final project-level license statement if needed.

Citation

If you use this checkpoint, please cite the LRAT paper.

@misc{lrat2026,
  title        = {Learning to Retrieve from Agent Trajectories},
  author       = {TODO},
  year         = {2026},
  howpublished = {Manuscript in preparation},
  note         = {arXiv link to be added}
}

Links

• Paper: TODO
• Project page: TODO
• Code: https://github.com/Yuqi-Zhou/LRAT
• Model: https://huggingface.co/Yuqi-Zhou/LRAT-multilingual-e5-large
• Dataset: https://huggingface.co/datasets/Yuqi-Zhou/LRAT-Train
• Companion Qwen3 checkpoint: https://huggingface.co/Yuqi-Zhou/LRAT-Qwen3-Embedding-0.6B