Steve0927/qwen3-embedding-0.6b-lrat-full80k-conservative-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. TUC performs conservative row-internal auditing of each official query-positive-negative row: weak positives are downweighted or removed, high-confidence false-negative negatives are removed, and sample weights remain bounded.
Data Boundary
Training data source:
data/processed/lrat_broad_g16_v2_llm_full80k_conservative_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 and confidence>=0.75; min_neg=4
Gate Evidence
Full80k gate assessment:
- Verdict:
pass - Report:
reports/full80k_gate_assessment.json
Dense gate:
- Report:
reports/dense_gate_full80k_conservative_from_broad.json - Verdict:
pass - Vs broad-g16-v2:
recall@20 +0.0079996,recall@50 +0.0011203,hit@50 +0.0156627 - Vs v5:
recall@20 +0.0014922,recall@50 +0.0002979,hit@50 +0.0120482
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.