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Steve0927/
qwen3-embedding-0.6b-lrat-full80k-hardneg-preserve-mr2-min6-from-broad

Name: qwen3-embedding-0.6b-lrat-full80k-hardneg-preserve-mr2-min6-from-broad
Availability: InStock

sentence-transformers

Qwen/Qwen3-Embedding-0.6B

finetune

text-embeddings-inference

endpoints_compatible

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Steve0927/qwen3-embedding-0.6b-lrat-full80k-hardneg-preserve-mr2-min6-from-broad

This is a Qwen3-Embedding-0.6B compatible dense retriever checkpoint prepared for XIR competition 1170.

Method

The method is Trajectory Utility Calibration (TUC).

We train only from official LRAT trajectory-derived retrieval rows. LRAT converts agent search/browse/reasoning trajectories into query-positive-negative training examples. TUC adds a conservative second-pass row-internal audit:

identify weak positives;
identify high-confidence false-negative negatives;
apply bounded sample weights;
preserve enough hard negatives for contrastive training.

Data Boundary

Training data source:

data/processed/lrat_broad_g16_v2_llm_full80k_hardneg_preserve_mr2_min6_weights.jsonl

Base checkpoint:

models/qwen3-embedding-0.6b-lrat-broad-g16-v2

Compliance boundary:

No generated queries.
No generated answers.
No generated passages or external evidence.
No A/B leaderboard qrels or hidden failures are used as training labels.
Strong models are used only as auditors over official LRAT query-positive-negative rows.

Training Configuration

Learning rate: 3e-7
Epochs: 0.5
Group size: 16
Batch per device: 24
Thresholds: drop_low_pos_quality<=1.0; false_negative relevance>=3.5 confidence>=0.75; max_remove_per_row=2; min_neg=6; weight=[0.4,2.0]

Gate Evidence

Full80k gate assessment:

Verdict: pass
Report: reports/full80k_gate_assessment.json

Dense gate:

Report: reports/dense_gate_full80k_hardneg_preserve_mr2_min6_vs_full80k_conservative.json
Summary: pass vs broad_g16_v2 and pass vs full80k_conservative; recall@50 +0.001185 vs current best; hit@50 flat

Intended Use

This checkpoint is intended as a dense retriever in the XIR/BrowseComp-Plus-style fixed-corpus agent-search setting, where the submitted artifact is only the retriever checkpoint and the agent/judge/runtime are fixed externally.

Limitations

The checkpoint is optimized for agentic retrieval from LRAT-style trajectory supervision.
It should not be interpreted as a general-purpose factual QA model.
Leaderboard results should be used only as checkpoint gates, not as sources for new training labels.