EKLAV: Elicited Key CoT Learning via Answer only superVision

arXiv:2607.25182 · cs.CL, cs.AI, cs.IR · Submitted 2026-07-28 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: I'm Tom.

Jane: And I'm Jane, here with Lu, Meng and our in-house large language model, Lalam.

Tom: We're looking at the paper: "EKLAV: Elicited Key CoT Learning via Answer only superVision".

Jane: The gist The ability to retrieve relevant tables for answering questions is a key task for structured information retrieval,

Tom: First, who's behind it and why it matters.

Title and authors: Tom: We’re looking at "EKLAV: Elicited Key CoT Learning via Answer only supervision." That title tells you immediately that they aren't just trying to copy teacher reasoning traces, which is what a lot of the literature has done before. They call it elicited key chain of thought learning.

Jane: Elicited key chain of thought learning sounds complicated, so can you break that down for us? What’s the main goal behind focusing on eliciting just the "key" parts instead of everything?

Lu: The paper says they explore two variants of training a compact reasoning model based on their teacher's outputs. They look at CoTGen distillation, which is where the student model tries to reproduce the teacher's complete reasoning trace along with the final prediction.

Meng: So that’s what we discussed before—the approach that tries to copy the whole reasoning path. And they are saying that this might lead to overfitting on specific dataset patterns #pg2.

Lalam: Exactly, and they point out that this approach can cause degraded cross-domain transferability, especially when you move to a different set of tables or questions #pg2.

Tom: So the key is moving away from that full reproduction. The paper sets up a comparison between CoTGen and two other approaches: Conditional Reasoning Distillation, or CoTCond, and Naive SFT.

The paper's summary: Jane: Okay, so they compare three training paradigms for their student models. Could you tell us what the difference is between Naive SFT, CoTGen distillation, and this Conditional Reasoning Distillation?

Lu: Naive SFT trains the base model using only the final ranked output generated by the teacher model. They remove all reasoning traces entirely from that training process #pg6.

Meng: So they’re saying just training on the answer is fine, but it loses all that valuable reasoning structure, which we know isn't ideal for complex retrieval tasks.

Tom: Then you have CoTGen distillation, where the model is trained to generate both the reasoning trajectory and the final ranking output simultaneously. They compute loss over both tokens #pg6.

Lalam: And then there’s CoTCond, which is the one they really focus on for this work. With CoTCond, they train the model only to predict that final ranking conditioned on the provided reasoning context, but they don't compute any loss over the reasoning tokens themselves #pg6.

Jane: So CoTCond is essentially trying to get those benefits of having reasoning supervision without getting bogged down by the risk of overfitting to a specific teacher’s thought process #pg4.

Tom: That's the fundamental tension they call it: leveraging intermediate signals without explicitly imitating the teacher’s reasoning trajectory, which decouples structured reasoning from overfitting risks #pg4.

The paper's improvements: Tom: Now let’s talk about what they found in terms of performance. Their conditional CoT distillation strategy substantially outperforms both standard supervised fine-tuning and CoTGen when generalizing to out-of-distribution table settings #pg4.

Jane: What kind of improvements are we actually looking at? Are we seeing big jumps on the numbers, or is it just a slight tweak?

Lu: They show significant gains across several benchmarks. On HybridQA, they see an increase in Acc@ten by thirty point five percent #pg4. On SQA, that's a fifteen point two percent increase #pg4. And on TabFact, they get a fifty-two point nine percent improvement #pg4.

Meng: That level of improvement across different datasets sounds promising for real-world deployment, but what about the complexity? Does this still run fast enough when we’re dealing with massive tables?

Tom: The paper also points to inference efficiency. Because CoTCond doesn't require generating the full reasoning trace at inference time, it produces substantially shorter reasoning traces compared to CoTGen models #pg10. This means lower latency and reduced generation cost #pg10.

Lalam: It seems like the main benefit isn't necessarily refining the fine-grained ranking order more than other methods, but rather eliminating structural failures in the output #pg9.

Conclusion: Jane: So to wrap up, it sounds like EKLAV shows that conditioning on teacher reasoning provides a reliable way to inject structured knowledge into table reranking without the risk of overfitting to those specific reasoning paths. What’s the final word from you guys?

Tom: The main thing is that this approach improves generalization and ranking accuracy across diverse settings, especially in multi-table retrieval scenarios #pg4. We're seeing cleaner reliability profiles overall because it suppresses structural failures, reducing duplicate predictions down to two point two percent on TAT-QA and below three percent on every other dataset #pg9 <ref:2607.25182#pg1>.

Lu: From a research standpoint, this reframes how we think about reasoning distillation for listwise reranking, showing that conditioning on teacher reasoning is a strong mechanism #pg9.

Meng: For us in production, the lower inference cost from shorter traces and the reduced structural errors are what matter most when we’re trying to ship something reliable. That makes CoTCond quite appealing #pg10.

Lalam: I'm confident that this method is a solid step forward because it leverages teacher reasoning without forcing the model to generate long, error-prone sequences #pg10. We should definitely keep an eye on how this applies beyond just text-based retrieval.

Adarsh Singh, Kushal Raj Bhandari, Jianxi Gao, Soham Dan, Vivek Gupta

Arizona State University · Rensselaer Polytechnic Institute · Scale AI

cs.CL, cs.AI, cs.IR

Submitted: 2026-07-28

Updated: 2026-10-16

Comments: 8 pages, 3 figures

Code: https://github.com/AdarshSingh7647/Eklavya

License: http://creativecommons.org/licenses/by/4.0/

The gist: The gist The ability to retrieve relevant tables for answering questions is a key task for structured information retrieval, and TabRank presents a framework for training reasoning rerankers for

Key concepts

EKLAV: A framework designed to elicit key Chain-of-Thought (CoT) learning from large language models by focusing the training signal solely on the final correct answer. This approach aims to teach reasoning capabilities without requiring the model to generate lengthy, explicit intermediate reasoning steps during training.
Answer Only Supervision: A training paradigm where a model is supervised only on the correct final output (the answer) of a question, instead of being trained on the entire sequence of reasoning tokens. This simplifies the supervision signal and focuses learning on achieving high accuracy in reaching the right conclusion.
Key CoT Learning: The specific type of reasoning skill that EKLAV seeks to instill in models. Instead of forcing a model to write out every intermediate thought, this technique aims to teach the model *which* reasoning steps are most critical for solving a problem, allowing it to perform complex tasks efficiently.
Distillation Strategy: The process used within EKLAV where knowledge from a 'teacher' model is transferred to a 'student' model. In this context, it involves distilling the essential reasoning insights derived from the teacher's answer-based reasoning into the student model, enhancing its ability to perform similar reasoning tasks.

Terminology

Trace — 3× in this paper · explained in 2 episodes
Reasoning Traces — 2× in this paper · explained in 4 episodes
Benchmark — 1× in this paper · explained in 2 episodes
Chain-of-Thought (CoT) — 1× in this paper · explained in 5 episodes
Chain-of-Thought (CoT) Reasoning — 1× in this paper · explained in 4 episodes
Distillation — 1× in this paper · explained in 2 episodes
Generalization — 1× in this paper · explained in 13 episodes
Semantic Understanding — 1× in this paper · explained in 2 episodes

Summary

The gist The ability to retrieve relevant tables for answering questions is a key task for structured information retrieval, and TabRank presents a framework for training reasoning rerankers for Tabular Retrieval that significantly improves performance across various table retrieval datasets by employing conditional reasoning distillation.

How it works

TabRank introduces a conditional reasoning distillation framework where the student reranker is conditioned on the teacher’s reasoning context within the prompt rather than being supervised to generate teacher reasoning traces token-by-token<ref:2607.25182#pg4>. This formulation "encourages the model to leverage intermediate reasoning signals without explicitly imitating the teacher’s reasoning trajectory, thereby decoupling structured reasoning benefits from the overfitting risks associated with reasoning trace generation"<ref:2607.25182#pg4>. The input structure for this method is defined as x = [q; T; r], where r denotes the teacher-generated reasoning trace<ref:2607.25182#pg4>.

Training and Distillation Strategies

The paper explores three distinct training paradigms to train the base model using synthetic reasoning data<ref:2607.25182#pg6>. These include Naive SFT, Standard Chain-of-Thought Distillation (CoTGen), and Conditional Reasoning Distillation (CoTCond)<ref:2607.25182#pg6>.

Naive SFT trains the base model using only the final ranked output generated by the teacher model, removing reasoning traces entirely<ref:2607.25182#pg6>.
Standard Chain-of-Thought Distillation (CoTGen) trains the model to explicitly generate both the reasoning trajectory and the final ranking output, computing loss jointly over reasoning and ranking tokens<ref:2607.25182#pg6>.
Conditional Reasoning Distillation (CoTCond) trains the model only to predict the final ranking conditioned on the provided reasoning context, with no loss computed over reasoning tokens themselves<ref:2607.25182#pg6>.

Performance and Generalization

TabRank's conditional CoT distillation strategy substantially outperforms both standard supervised fine-tuning and CoTGen in generalizing to out-of-distribution table settings, including multi-table retrieval and financial-domain datasets such as TATQA<ref:2607.25182#pg4>. The approach significantly improves performance across a variety of table retrieval datasets, increasing Acc@10 by 30.5% on HybridQA, 15.2% on SQA, 52.9% on TabFact, and 13.1% on TATQA subsets of the Multi-Table QA Benchmark compared to the base model<ref:2607.25182#pg4>. Notably, TabRank generalizes effectively to multi-table reasoning<ref:2607.25182#pg4>.

Error Analysis and Reliability

The error analysis shows that CoTCond achieves the cleanest reliability profile overall, nearly eliminating structural failures across all benchmarks, reducing duplicate predictions to 2.2% on TAT-QA and below 3% on every other dataset<ref:2607.25182#pg9>. This suggests that conditioning on teacher reasoning instead of generating it token-by-token preserves the benefits of reasoning supervision while avoiding the long generation chains responsible for many CoTGen failures<ref:2607.25182#pg4>. The results show that most improvements from reasoning distillation come from eliminating structural failures rather than refining fine-grained ranking order<ref:2607.25182#pg9>.

Conclusion

In this paper, TabRank presents a reasoning-aware framework for table reranking that includes a dataset of 6,728 synthetic reasoning traces for NQ-Tables Reranking and a set of compact distilled reranking models for tabular retrieval<ref:2607.25182#pg10>. We demonstrate that incorporating reasoning supervision significantly improves retrieval quality across a diverse collection of out-of-domain and multitable question answering benchmarks<ref:2607.25182#pg4>. In particular, our proposed conditional reasoning distillation strategy allows models to leverage teacher-generated reasoning without explicitly reproducing long reasoning traces, resulting in better generalization, stronger ranking accuracy, and lower inference cost<ref:2607.25182#pg10>. TabRank also reduces structural generation failures such as malformed outputs and duplicate predictions, leading to more reliable reranking behavior<ref:2607.25182#pg10>. This finding reframes the contribution of reasoning distillation for listwise reranking<ref:2607.25182#pg9>.

Limitations

Our work focuses exclusively on text-based table retrieval and assumes that tables are available in a structured serialized format<ref:2607.25182#pg11>. Additionally, our training setup relies on synthetic reasoning traces generated by a single teacher model, which may also inherit biases or reasoning artifacts specific to the teacher<ref:2607.25182#pg11>. Finally, our models are trained primarily on English-language benchmarks and evaluated on question answering and fact checking workloads<ref:2607.25182#pg11>.

Ethics Statement

This work focuses on improving table reranking models for question answering and information retrieval research<ref:2607.25182#pg11>. Our approach relies on synthetic reasoning traces generated by large language models, and we encourage careful evaluation of reasoning-based retrieval systems before deployment in high-stakes or real-world applications<ref:2607.25182#pg11>. The work explores conditional reasoning distillation techniques that improve efficiency while reducing inference overhead<ref:2607.25182#pg10>.

Evaluation Metrics

We report three standard information retrieval metrics, each computed at cutoff K. Recall@K measures the fraction of relevant items recovered within the top-K retrieved results. nDCG@K evaluates ranking quality by assigning higher credit to relevant items appearing at earlier positions via a logarithmic position discount. Accuracy@K measures the proportion of queries for which all relevant items are retrieved within the top-K results.

Prompt Template

The prompt used for data generation is structured as follows. You are a table relevance expert. Given a question and a set of candidate tables, rank them from most to least useful for answering the question. Output your final ranking as JSON:

Output your final ranking as JSON:

The prompt template requires the model to output its final ranking in a specific JSON format. The required output format is given by the template.

Dataset Statistics

The statistics for the datasets used in our experiments are summarized in Table 2. The dataset statistics show that TabFact has 34,351 tables and HybridQA has 17,229 tables.

References

The references listed are as follows<ref:2607.25182#pg11>. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Zhang, Shirong Ma, Xiao Bi, et al. 2025. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948<ref:2607.25182#pg11>. Jonathan Herzig, Thomas Müller, Syrine Krichene, and Julian Eisenschlos. 2021. Open domain question answering over tables via dense retrieval. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 512–519, Online<ref:2607.25182#pg11>. Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al. 2024. Openai o1 system card. arXiv preprint arXiv:2412.16720<ref:2607.25182#pg11>. Nour Jedidi, Yung-Sung Chuang, James Glass, and Jimmy Lin. 2025. Don’t overthink passage reranking: Is reasoning truly necessary? arXiv preprint arXiv:2505.

Improvements for AI systems

textbfConditional Reasoning Distillation (CoTCond) for Table Reranking Stability and Generalization: The improved system can leverage teacher-generated reasoning without explicitly reproducing long reasoning traces, resulting in better generalization, stronger ranking accuracy, and lower inference cost. This addresses the fundamental tension between imitating teacher traces (CoTGen) and avoiding overfitting risks associated with trace generation.
textbfImproved Reliability via Error Suppression: The system nearly eliminates structural failures across all benchmarks, reducing duplicate predictions to 2.2% on TAT-QA and below 3% on every other dataset. This is achieved by conditioning on teacher reasoning instead of generating it token-by-token, which suppresses errors like duplicate outputs that plague base models.
textbfEnhanced Performance in Out-of-Distribution Settings: The model "significantly improves performance across a variety of table retrieval datasets, increasing Acc@10 by 30.5% on HybridQA, 15.2% on SQA, 52.9% on TabFact, and 13.1% on TATQA subsets of the Multi-Table QA Benchmark compared to the base model." This demonstrates effective generalization to novel table layouts and multi-table scenarios without architectural modification.
textbfIncreased Inference Efficiency: The method produces substantially shorter reasoning traces at inference time, resulting in lower latency and reduced generation cost compared to CoTGen-style models. This contrasts with CoTGen which produces the longest outputs (e.g., 3716 tokens on TabFact), making the system faster and cheaper for real-time applications.

Abstract

Reasoning distillation trains a student to reproduce both the teacher's chain-of-thought trace and its final answer. We ask whether the trace is better used as privileged training context than as an imitation target. EKLAV keeps the teacher's reasoning in the input sequence but supervises only the final task answer, applying a task-dependent placement that moves part or all of the trace into the prompt. On BRIGHT passage reranking, EKLAV improves average nDCG@10 at all four tested checkpoints across two model families, winning 41 of 48 model-domain comparisons while reducing training compute by up to 31.6% and inference time by up to 32.7%. On four held-out table-reranking benchmarks, it is the best of four conditions on three, exceeding full-trace distillation by 3.0 points and answer-only training without reasoning by 5.5 points on average. Analysis of generated traces reveals that EKLAV students defer their first verbalized verdict later (0.76 vs. 0.44 relative position), reverse fewer decisions (5% vs. 16%), and produce reasoning more predictive of their own rankings than STD-COT's, though less predictive than the base model's. The recipe reuses a fixed teacher corpus, requires no student rollouts or iterative teacher feedback, and applies to both pointwise and listwise formulations. Code and Data are at https://github.com/AdarshSingh7647/Eklavya