Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markers

We thank R5ToD for noting that our work reports “significant improvements on tasks such as open generation, code repair, and length instruction following.” We welcome the reviewer's view, and think many of their points can be addressed below.

Can 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?

We thank R5ToD for the opportunity to clarify the methodological distinctions between our framework and prior input labeling methods. While some of the cited works use input tags, our approach has significant differences in both scope and flexibility. Rather than designing task- or dataset-specific tags [4] [6] [17], we introduce a general-purpose taxonomy of interpretable, natural language markers, learned during supervised fine-tuning. In contrast to [6] which does not enable inference time-control, our marker dropout strategy during training enables the model to generalize and self-condition when no markers are provided—something not addressed by earlier work. We provide more explicit comparison with the work recommended by R5ToD below:

[4] uses a domain-specific token appended to the target during training. In contrast, we apply markers to both prompts and outputs, allowing for optional control at inference time, enabled by dropout-based regularization.

[6] tags bitext with quality labels for supervised training, but does not enable inference-time control. Our method allows for (completely optional) inference-time use, giving users flexibility while retaining model performance.

[17] adds rigid prompt tokens (e.g., "2es") for translation direction. We use interpretable natural language markers instead, which support broader attributes and better generalization across tasks.

[61] focuses solely on length control using a custom dataset and DPO. In contrast, we learn length as one of many attributes across all training data, achieving strong control without additional objectives or tuning stages, and preserving overall generation quality.

Given 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?

We appreciate R5ToD’s concern and would like to clarify that our framework does not rely on any proprietary aspects of the models or data used. The approach we propose—adding interpretable, natural language markers directly into the training data for supervised fine-tuning—is simple, transparent, and model-agnostic. There are no model-specific training strategies or dependencies that would prevent this method from being applied to publicly available models such as LLaMA or Gemma. We see our work as a general framework that can readily transfer to any model and would welcome the reviewer’s input if there are specific concerns or model families where they believe this approach may not generalize.

In 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?

We thank R5ToD for this question. In Figure 4, we intentionally use the same reward model for both training and evaluation in order to isolate whether “quality” can be effectively learned as a latent attribute during training. Introducing a third-party reward model would add confounding variables—particularly the alignment (or lack thereof) between the annotating and evaluating RMs—making it harder to isolate the effect of our method.

That said, we note that in other parts of the paper—specifically the open-ended generation (Section 3.1) and length control evaluation (Section 3.3)—we do use an external reward model (GPT-4o) to assess performance, providing an independent signal of the model’s controllability and generalization. We observe consistent results of gains across both scenarios.

What 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?

We appreciate R5ToD’s thoughtful question. To assess the reliability of the Command R+ model used for labeling, we ran tagging accuracy checks on datasets that included annotations—for instance, we normalized the “subset” attribute in the MMLU dataset to the marker values in the taxonomy. Using the prompt described in the Appendix, we observed an average tagging accuracy of over 64.9%. Many of the remaining mismatches were explainable—for instance, examples labeled as "Classification" could also be reasonably interpreted as "QuestionAnswering," as these categories may overlap for some examples. While some level of noise in automatic annotation is inevitable, our downstream performance lifts suggest that our technique is remarkably robust to some degree of labelling noise. We observe consistent performance gains across multiple tasks, which is ultimately the end-goal of our work.

We thank R5ToD again for their engagement with our work, and we welcome continued engagement during the rebuttal period.

We thank RxUkW for highlighting that our method addresses the "long-tail performance gap." We also appreciate that RxUkW recognizes that this intervention is extremely efficient and "deployment-friendly", such that “steering the model requires no extra forward passes, contrastive decoding, or RL at inference time.”

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.

We thank RxUkW for highlighting the need for clarification. While our approach may appear similar to prior work on the surface, it differs significantly in both motivation and practical use.

Prompt- and prefix-tuning are parameter-efficient fine-tuning strategies designed to reduce the computational cost of supervised fine-tuning (SFT), particularly in task-specific settings. However, these methods do not support transparent inference-time control. They rely on learned soft vectors, are generally opaque, and lack generalization beyond specific task boundaries. Control codes are dataset-specific, non-extensible and require careful dataset curation to function effectively—limiting flexibility across domains and tasks.

In contrast, our framework introduces interpretable, natural language markers directly into the training data. This enables:

1. Inference-time control using human-readable markers that correspond to semantic attributes (e.g., style, domain).
2. Flexible usage—inference-time markers are optional. When omitted, the model can self-condition by predicting its own markers. This removes the burden from the end practitioner to grapple with complex prompt engineering, and makes more explicit what conditioning is inferred from the prompt.
3. Multi-task generalization—our taxonomy-based setup supports learning across diverse datasets and tasks within a single model. As shown in Section 4, novel marker combinations at inference can yield additional improvements, even if not seen during training.

We respectfully clarify that we do compare to SFT which typically outperform prompt- and prefix-tuning methods in general-purpose settings.

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.

Thank you for raising this concern. RxUkW is correct that using reward model (RM) scores as markers means any bias in the RM may propagate into training and inference. However, to show that this does not limit the utility of an estimate of generation quality, we separate the pretrained RM used to derive the quality markers from the models used to evaluate the quality lift. For evaluation, we used GPT-4o to evaluate the gains in Arena-Hard (Sec 3.1) and AlpacaEval-Length-Instructed(LI) (Sec 3.3) benchmarks. This is important, because GPT-4o does not present the same bias, and yet shows consistent gains in overall quality (5.7% absolute improvements on ArenaHard and 6.86% absolute improvements on AlpacaEval-Length-Instructed). This suggests that the bias introduced by using a reward model to assess quality is greatly outweighed by the benefits of having a flexible and useful relative measure of quality to guide inference conditioning.

The 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.

We note that there is indeed a “Limitations” section discussed in Appendix D (Supplementary Material), where we highlight promising directions for future work, including scaling to larger models, expanding the taxonomy, and exploring integration into pre-training. We also reference prior work in Section 5 (Related Work) and Appendix C (Extended Related Work). We moved these to the appendix because we were limited in space.

We thank RxUkW again for their engagement with our work, and we welcome continued engagement during the rebuttal period.

We thank RAB9d for describing our approach as “intuitive” and for recognizing its ability to “reduce user burden” while offering control through a "comprehensive taxonomy" and training-time augmentation leading to performance gains even on the long tail.

How accurate are the LLM-inferred markers during training? Is there scope for improvement in the quality of the markers?

We appreciate RAB9d’s attention to this aspect of our work. To assess the reliability of the Command R+ model used for labeling, we ran tagging accuracy checks on datasets that included annotations—for instance, we normalized the “subset” attribute in the MMLU dataset to the marker values in the taxonomy and observed an average tagging accuracy of over 64.9%. We note that even without fully accurate annotation, our approach yields consistent performance gains on downstream tasks (5.7% absolute improvements on ArenaHard, 35.3% violation reduction on AlpacaEval-Length-Instructed evaluations and 14.1% relative improvements on underrepresented coding tasks like CodeRepair). This is actually a strength of the method as it means we can cheaply rely on LLM annotations while still ensuring downstream performance lifts.

What happens if the user enters a wrong or unknown marker at inference time? Does the model still recover?

We thank RAB9d for the insightful question. One of the strengths of our framework is that providing markers at inference time is entirely optional, which significantly reduces the burden on the user and minimizes the risk of inputting incorrect or unknown markers. Table 7 demonstrates that even though the model(50_50) predicted markers aren’t perfect (68.8% over domain, task, format and language), the model still recovers and leads to consistent performance gains on downstream tasks. The regularization effect introduced by 50% dropout of markers in the input space during training makes it robust to the lack of markers during inference. In cases where unknown or completely irrelevant markers are used at inference, we find that the model tends to ignore them gracefully and still predicts the appropriate marker.

Please add absolute values not just the improvements for Figure 5 (right).

We are very happy to include the absolute values for Figure 5 in the final version of the manuscript, and commit to including it in the camera ready.

We thank RAB9d again for their engagement with our work, and we welcome continued engagement during the rebuttal period.

We thank RHdba for finding our “empirical gains significant” especially on “underrepresented domains”. RHdba recognized the breadth of our experiments, including open-ended generations (~5.7 % average win-rate uplift), code generation and machine translation. The reviewer notes the large gains in targeting long tail performance (Over 9.1 % lift).

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.

We thank RHdba for the opportunity to clarify the contribution of our work. While the taxonomy is an important aspect of the proposed framework, the technical contribution of our work extends beyond the taxonomy. We show that extremely cheap interventions of adding treasure markers results in a model learning to infer long tail characteristics. Despite requiring only minimal changes to the standard training workflow, these targeted interventions prove highly effective. The novelty is that the resulting framework is flexible, enabling inference gains in underrepresented domains and control of targeted attributes—improvements that would otherwise demand more costly alternatives such as data rebalancing during training, prompt engineering, or manual optimization of data compositions.

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?

We thank RHdba for the opportunity to elaborate on the taxonomy. Our goal was to study a wide variety of markers encompassing both deterministic properties (like length, language) and LLM-annotated (like domain, task) to characterize the SFT data mix. Furthermore, we wanted to test gains across a wide array of well represented and low frequency features, and hence chose a taxonomy that would characterize this skew (type of coding task, domain which are heavily skewed markers as described in Sec 3.2.1 and 3.1 respectively).
We would like to clarify that there are 13 main marker categories, each of which includes subcategories within the taxonomy, resulting in a total of 90 possible values. Additional details are available in the Appendix A, and we’re happy to surface more of that in the main text if the reviewer finds it helpful.

We thank RHdba again for their engagement with our work, and we welcome continued engagement during the rebuttal period.