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Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markers
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Proposes Treasure Markers: training-time metadata tags (quality, length, domain, task, format, language, etc.) appended to prompts and prepended to completions, with marker dropout so the model can infer tags at inference, improving long-tail performance and controllability of a 7B model.
Problem
LLMs perform poorly on rare, underrepresented use cases in training data, and inference-time prompt engineering or few-shot examples are brittle and burdensome for users.
Approach
Attach a large taxonomy (~90 markers across 13 categories) of data attributes as natural-language training markers in both input and output, with dual dropout (dataset- and sample-level) so markers can be inferred at inference or set manually or auto-annotated on the fly by an LLM.
Key results
- Win rate vs Gemma2-9B rises 5.7 points (32.1% to 37.8%) on Arena-Hard with inferred markers, with +9.1% in underrepresented domains.
- Length violation on AlpacaEval-LI drops from 36.58% to 1.25% with fixed length markers, and to 0.75% with on-the-fly markers annotated by Command A.
- CodeRepair gets up to 14.1% relative gain; language control line-level pass rate averages +10.98 points across 14 languages.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train with multidimensional markers and infer them at inference | Proprietary 7B · Arena-Hard-Auto | Win Rate (%): 32.1% to 37.8%; +5.7% | Helped |
| Use inferred training markers | TreasureMarked · Arena-Hard-Auto, underrepresented domains | Win Rate (%): +9.1% | Helped |
| Set <quality> and <quality_bucket> markers at inference | TreasureMarked · m-Arena Hard | Win Rate (%): 48.21% → 56.5% | Helped |
| Use targeted markers for rare code tasks | TreasureMarked · HumanEvalPack, CodeRepair | pass@1: 14.1% relative improvement | Helped |
| Use targeted markers for rare code tasks | TreasureMarked · HumanEvalPack, CodeTranslation | pass@1: 6.5% relative gain | Helped |
| Infer markers from the length-constrained instruction | TreasureMarked · AlpacaEval-LI | Violation (%): 36.58% → 24.74% | Helped |
| Supply the length_tokens marker at inference | TreasureMarked · AlpacaEval-LI | Violation (%): 36.58% → 1.25% | Helped |
| Use training markers at inference | TreasureMarked (fixed) · WMT'24++, en→ro | XCOMET-XL: .8048 → .8166; (+1.18) | Helped |
| Use training markers at inference | TreasureMarked (fixed) · Language Confusion Benchmark, Complex Prompts | Line-level Pass Rate (LPR): 68.6 → 79.58; ↑ 10.98 | Helped |
| Have Command A annotate markers on-the-fly (ablation) | TreasureMarked · AlpacaEval-LI | Violation (%): 24.74% → 0.75% | Helped |
| Have Command A annotate markers on-the-fly (ablation) | TreasureMarked · AlpacaEval-LI | Win Rate (%): 19.48% to 21.85% | Helped |
| Add the <domain> marker alongside <length_tokens> (ablation) | TreasureMarked (fixed) · AlpacaEval-LI | Win Rate (%): 21.22% → 24.72%; +3.5% | Helped |
Surprising findings
- Model learns length constraints from markers despite never seeing natural-language length instructions in training prompts.
- Without dataset-level marker dropout (0%), marker inference accuracy at inference is very low (3.42%).
Limitations
- Experiments limited to a 7B proprietary model; larger scales untested.
- Only instruction fine-tuning evaluated; pretraining-stage markers not explored.
- Relies on proprietary reward model and proprietary data; taxonomy design choices may not generalize.
Experimental setup
Datasets: proprietary instruction-style data mixture (2.7M examples) AlpacaEval-LI HumanEvalPack
Benchmarks: Arena-Hard-Auto m-Arena Hard AlpacaEval-LI HumanEvalPack WMT'24++ Language Confusion Benchmark
Metrics: win rate pass@1 length violation rate XCOMET-XL line-level pass rate marker accuracy
Baselines: Baseline model without markers Gemma2-9B
Builds on: Arena-Hard-Auto HumanEvalPack AlpacaEval-LI
Closest papers by meaning
- Breaking Long-Tailed Learning Bottlenecks: A Controllable Paradigm with Hypernetwork-Generated Diverse Experts (NeurIPS 2024 · similarity 0.86)
- Towards Heterogeneous Long-tailed Learning: Benchmarking, Metrics, and Toolbox (NeurIPS 2024 · similarity 0.86)
- Portable Reward Tuning: Towards Reusable Fine-Tuning across Different Pretrained Models (ICML 2025 · similarity 0.85)
- LT-Soups: Bridging Head and Tail Classes via Subsampled Model Soups (NeurIPS 2025 · similarity 0.85)
- DiffuLT: Diffusion for Long-tail Recognition Without External Knowledge (NeurIPS 2024 · similarity 0.85)
- Let's (not) just put things in Context: Test-time Training for Long-context LLMs (ICLR 2026 · similarity 0.84)
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