Title: Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markers
Venue: NeurIPS 2025
Accepted: yes
Keywords: large language models

controllability

generation conditioning

data taxonomy
Abstract: One of the most profound challenges of modern machine learning is performing
well on the long-tail of rare and underrepresented features. Large general-purpose
models are trained for many tasks, but work best on high-frequency use cases.
After training, it is hard to adapt a model to perform well on specific use cases
underrepresented in the training corpus. Relying on prompt engineering or few-shot
examples to maximize the output quality on a particular test case can be frustrating,
as models can be highly sensitive to small changes, react in unpredicted ways
or rely on a fixed system prompt for maintaining performance. In this work, we
ask: Can we optimize our training protocols to both improve controllability and
performance on underrepresented use cases at inference time? We revisit the divide
between training and inference techniques to improve long-tail performance while
providing users with a set of control levers the model is trained to be responsive
to. We create a detailed taxonomy of data characteristics and task provenance to
explicitly control generation attributes and implicitly condition generations at
inference time. We fine-tune a base model to infer these markers automatically,
which makes them optional at inference time. This principled and flexible approach
yields pronounced improvements in performance on examples from the long tail
of the training distribution. Overall, we observe lifts of 5.7% across all tasks.
However, treasure markers are particularly effective at finding difficult to obtain
gains in the long-tail. We observe relative lifts of up to 14.1% on underrepresented
tasks like CodeRepair and absolute improvements of 35.3% on length instruction
following evaluations.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new method called \"treasure markers\" to improve the controllability of LLMs at inference time and the performance on long-tail distribution tasks by adding detailed markers during training. The method aims to reduce the burden of prompt engineering for users and allow the model to automatically infer these markers. The authors report significant improvements on tasks such as open generation, code repair, and length instruction following.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper explicitly states that the code, model, and data have not been released, and the reproducibility is weak.\n\nThe paper admits that \"we only report the significance test of the machine translation experiment in Section 3.4\". This means that, in addition to machine translation, other key results, such as open generation performance improvement, long-tail task improvement, and length control improvement, do not provide error bars or statistical significance tests. Only a small number of experimental results in Table 4 are significant; most of the results are not significant, and some are even negative.\n\nThe paper mainly compares the proposed method with an internal baseline model (a model trained on the same data but without adding tags). Although this internal comparison is clear, the paper fails to fully demonstrate the competitiveness of its method on all tasks relative to the current state-of-the-art (SOTA) external model or a more powerful baseline.\n\nThe concept of using labels or tags to improve model performance is not new and has been applied in early generation models. The paper claims that its contribution lies in a \"more general framework\" and \"super detailed tags\", but this is more like an extension and scaling of existing methods rather than a fundamental innovation. Most importantly, I didn’t see any significant improvement, and the experiment was very unsolid.\n\nQUESTIONS:\nCan the authors elaborate on the fundamental methodological differences between your framework and the input labeling methods in previous work (e.g., [4], [6], [17], [61], etc.), in addition to using a more exhaustive set of labels?\n\nGiven the use of proprietary models and data in this paper, do the authors plan to validate the method on publicly available models (e.g., Llama, Gemma) and datasets to enhance the credibility of the conclusions?\n\nIn the quality control experiment (Figure 4), the evaluation and training labels are from the same source. Can the authors provide data using an independent third-party reward model or human evaluation to more objectively demonstrate the model's ability to control quality?\n\nWhat is the accuracy of the Command R+ model used to label the training data? Does the paper analyze the sensitivity of upstream labeling errors to the learning effect of downstream models? If the labeling quality is not high, will it mislead the training of the model?\n\nLIMITATIONS:\nNo code, models or data are publicly available", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a training protocol that embeds a rich set of textual markers directly into the instruction-tuning data, which describe length, quality, domain, task, language and more. During finetuning, the authors randomly drop these markers from inputs so the model learns to infer them; at inference, users may supply markers to steer generation for better quality.\n\nExperiments on 2.7 M instructions show that merely inferring markers already lifts win-rate against a strong baseline by 5.7% overall and by 9.1% on under-represented domains. Explicitly fixing markers yields task-specific boosts: CodeRepair accuracy rises 14.1% despite it is rare in training data; length-constrained generation violations decrease from 36.6% to 1.25%. These gains demonstrate that training-time markers simultaneously enhance long-tail performance and expose reliable, fine-grained controllability without heavy prompt engineering.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Merely asking the model to infer markers (no extra prompt tokens) raises overall win-rate against a strong baseline by +5.7%, and by +9.1% on under-represented domains, confirming that the approach attacks the long-tail performance gap it sets out to solve.\n2. Because the markers are short text tokens already in the vocabulary, steering the model requires no extra forward passes, contrastive decoding, or RL at inference time—making the method deployment-friendly.\n\nWeaknesses:\n1. The core idea—injecting markers into training data to steer generation—closely resembles prior work on prefix-tuning, control codes and prompt-tuning. The paper does not clearly present how “training-time markers” differ conceptually or practically from these well-established methods. In addition, while the paper shows gains over a baseline instruction-tuned model, it does not compare directly to other control mechanisms. Without such comparisons, it’s hard to assess relative efficacy.\n2. Quality markers are derived from a pretrained reward model’s scores. Any bias or inaccuracy in that reward model will propagate into both training and inference, potentially reinforcing undesirable behaviors rather than correcting them.\n\nQUESTIONS:\nWhy were direct comparisons against alternative controllability techniques omitted, can you include more experiments to clarify relative efficacy?\n\nLIMITATIONS:\nThe paper currently doesn't include “Limitations”. As highlighted in the Questions, I recommend that the authors include additional comparative experiments against a broader set of prior methods.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a new approach to train LLM to allow better control at inference time as well as to perform better on underrepresented use cases (long tail problem). The approach uses metadata tags such as length, task domain, language, quality and inserts these tags into the input and the output of training data. The model is then trained to both use and infer these markers, with dropouts applied on the input metadata tags to prevent over reliance. The approach is evaluated on several benchmarks like Arena-Hard, HumanEvalPack, AlpacaEval-LI, and WMT’24++ and demonstrates improved overall performance as well as enhanced performance on the long-tail tasks (e.g., CodeRepair).\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n1. Prompt tuning is becoming harder and harder for users and this paper’s proposed approach presents a more intuitive way for users to control the generation of LLMs. \n\n2. Compared to other inference time techniques to improve performance (e.g. few shot examples), this paper takes a different approach of augmenting the training process itself.  \n\n3. Training time markers has been done before (for e.g. AlphaCode), but this is first time it is done for a general-purpose dataset and on a more comprehensive taxonomy of about 90 tags.  \n\n4. Also, the markers can be inferred at inference time, so that means there is no extra burden on the user in normal cases, but also the user can intervene and inject tags if they want more control.  \n\n5. The approach shows performance gains on overall tasks as well as on the long tail of underrepresented tasks \n\n\n**Weakness**:\nNo major weaknesses but would love to learn more about how the approach be extended with additional markers in the future.  Would the approach scale to thousands of markers especially with domain specific markers for the different domains (for e.g. algorithm type for programming tasks, and clinical subdomain for medical)?\n\nQUESTIONS:\n- How accurate are the LLM-inferred markers during training? Is there scope for improvement in the quality of the markers? \n\n- What happens if the user enters a wrong or unknown marker at inference time? Does the model still recover? \n\n - Please add absolute values not just the improvements for Figure 5 (right).\n\nLIMITATIONS:\nYes, limitations are discussed.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

comments: SUMMARY:
The paper focuses on improving the controllability and performance on underrepresented use cases at inference time. Specifically, they construct a taxonomy of training markers and finetune the model. In inference time, they allow users to control the model with inference-time markers.

Empirically, the authors show significant performance gain on open-ended generation (~5.7 % average win-rate uplift). They also conduct experiments on specifica sub-tasks, including code generation, machine translation, etc.

STRENGTHS AND WEAKNESSES:
[Strength]
- the taxonomy of labels (e.g., data provenance, feature rarity, task type) could potentially be useful for future works
- the empirical gain is significant (~5.7 % average win-rate uplift in open-ended generation when using markers)
- the results show that the gain is more significant on underrepresented domains (Over 9.1 % lift)

[Weakness]
- The techinical contribution of the work is quite limited. It seems that most of the contribution is on the taxonomy construction. No novel training recipe or data construction methods are presented.
- The effectiveness of the method highly relies on the quality of the taxonomy. However, there are not much description about the construction process of the taxonomy in the main context. Like, how did you decide each node of the taxonomy? how did you adjust the taxonomy? what's the significance of all the 90 unique markers?

QUESTIONS:
None

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-06