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Foundational Automatic Evaluators: Scaling Multi-Task Generative Evaluator Training for Reasoning-Centric Domains

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.7Download Report PDF
Automatic evaluationLLM-as-judgemulti-task evaluatorsstep-level evaluationverifers
Abstract:

Finetuning specialized generative evaluators has emerged as a popular paradigm to meet the increasing demand for scalable evaluation during both training and test-time. However, recent work has largely focused on applying new methodology, such as reinforcement learning (RL), to training evaluators, shying away from large-scale, data-driven development. In this work, we focus on data scaling, curating a set of 2.5M samples spanning five unique evaluation tasks (pairwise, step-level, reference-free and reference-based verification, and single rating) and multiple domains focused on reasoning evaluation. With our data, we train Foundational Automatic Reasoning Evaluators (FARE), a family of 8B and 20B (with 3.6B active) parameter evaluators, with a simple iterative rejection-sampling supervised finetuning (SFT) approach. FARE-8B challenges larger specialized RL-trained evaluators and FARE-20B sets the new standard for open-source evaluators, surpassing specialized 70B+ evaluators. Beyond static benchmarks, we evaluate FARE in real-world tasks: As inference-time rerankers, FARE-20B achieves near-oracle performance on MATH. As verifiers in RL training, FARE improves the downstream RL-trained model performance by up to 14.1% vs. string-matching verifiers. When initialized from FARE, a continually-finetuned FARE-Code outperforms gpt-oss-20B by 65% on evaluating test-case quality

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces FARE, a family of generative evaluators trained on 2.5M samples spanning five evaluation tasks and multiple reasoning domains. It resides in the Multi-Task Evaluator Training Frameworks leaf, which contains only three papers including this work. This is a relatively sparse research direction within the broader taxonomy of 43 papers across 19 leaf nodes, suggesting the specific focus on large-scale data-driven multi-task evaluator training remains underexplored compared to adjacent areas like reasoning benchmarks or domain-specific applications.

The taxonomy reveals neighboring work in Multimodal Evaluator Design and Test-Time Compute Scaling for Evaluation within the same parent branch, alongside extensive activity in Reasoning Evaluation Methodologies covering step-level assessment and benchmark-free paradigms. The paper's emphasis on data scaling and supervised finetuning distinguishes it from these adjacent directions, which prioritize architectural diversity or inference-time computation. The scope_note for its leaf explicitly excludes single-task evaluators, positioning FARE as part of a push toward unified evaluation frameworks rather than specialized judges.

Among 20 candidates examined across three contributions, no clearly refuting prior work was identified. The multi-task dataset contribution examined 10 candidates with none providing overlapping prior work; the iterative rejection sampling approach examined 4 candidates with similar results; and the FARE model family examined 6 candidates without refutation. This limited search scope—20 papers from semantic search and citation expansion—suggests the analysis captures immediate neighbors but cannot claim exhaustive coverage of all potentially relevant multi-task evaluator training literature.

Given the sparse taxonomy leaf and absence of refuting candidates within the examined scope, the work appears to occupy relatively open ground in large-scale data-driven multi-task evaluator training. However, the limited search scale and the presence of only two sibling papers mean this assessment reflects local novelty within top-20 semantic matches rather than a comprehensive field survey. The taxonomy structure indicates active development in related evaluation methodologies, suggesting the broader evaluation landscape is evolving rapidly.

Taxonomy

Core-task Taxonomy Papers
43
3
Claimed Contributions
20
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: training multi-task generative evaluators for reasoning evaluation. This field addresses the challenge of automatically assessing complex reasoning outputs across diverse problem types, moving beyond traditional metrics toward learned evaluators that can handle multiple tasks simultaneously. The taxonomy reveals five major branches: Generative Evaluator Architecture and Training focuses on building and optimizing the evaluator models themselves, including multi-task training frameworks like those explored in Foundational Automatic Evaluators[0] and Praetor[1]; Reasoning Evaluation Methodologies and Benchmarks develops systematic approaches for measuring reasoning quality, as seen in Chain-of-Thought Evaluation[6] and Medical Reasoning Evaluation[31]; Reasoning Systems and Generative Models for Complex Tasks examines the reasoning capabilities being evaluated, from abstract reasoning in Unified Abstract Reasoning[8] to planning in Diffusion Planner[5]; Multi-Task Learning and Generative Model Applications explores broader multi-task architectures like Multi-Task Deep Generative[40] and Modular Multitask Reasoning[30]; and Domain-Specific Generative AI Applications targets specialized evaluation needs across fields from education in Responsible Generative AI Education[3] to earth observation in SAI4EO[21]. A particularly active tension exists between general-purpose multi-task evaluators and domain-specialized assessment frameworks, with works like Flex-Judge[18] and Autonomous LLM Evaluation[33] pushing toward flexible evaluation across tasks while others emphasize depth in specific reasoning types. Foundational Automatic Evaluators[0] sits squarely within the Multi-Task Evaluator Training Frameworks cluster, sharing conceptual ground with Praetor[1] and J4R[9] in developing unified training approaches for cross-task evaluation. Where Praetor[1] might emphasize particular architectural choices for multi-domain assessment, Foundational Automatic Evaluators[0] appears to establish core principles for training evaluators that generalize across reasoning types. This positioning reflects a broader shift in the field toward treating evaluation itself as a learnable multi-task problem, rather than relying on task-specific heuristics or human annotation at scale.

Claimed Contributions

Multi-task, multi-domain dataset for reasoning evaluation

The authors curate a dataset of 2.5 million samples spanning five evaluation tasks (pairwise, step-level, reference-free and reference-based verification, and single rating) across multiple reasoning-centric domains. This dataset combines existing high-quality annotations with newly generated synthetic data.

10 retrieved papers
Scalable iterative rejection sampling supervised finetuning approach

The authors introduce a training methodology using iterative rejection sampling with supervised finetuning that provides stable and efficient training at scale. This semi-online approach avoids distribution shift issues while remaining computationally tractable compared to full online RL methods.

4 retrieved papers
FARE family of foundational automatic reasoning evaluators

The authors develop FARE, a family of 8B and 20B parameter evaluators trained on their multi-task dataset. These models are evaluated on static benchmarks and real-world applications including inference-time reranking, verification during RL training, and domain adaptation.

6 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Multi-task, multi-domain dataset for reasoning evaluation

The authors curate a dataset of 2.5 million samples spanning five evaluation tasks (pairwise, step-level, reference-free and reference-based verification, and single rating) across multiple reasoning-centric domains. This dataset combines existing high-quality annotations with newly generated synthetic data.

Contribution

Scalable iterative rejection sampling supervised finetuning approach

The authors introduce a training methodology using iterative rejection sampling with supervised finetuning that provides stable and efficient training at scale. This semi-online approach avoids distribution shift issues while remaining computationally tractable compared to full online RL methods.

Contribution

FARE family of foundational automatic reasoning evaluators

The authors develop FARE, a family of 8B and 20B parameter evaluators trained on their multi-task dataset. These models are evaluated on static benchmarks and real-world applications including inference-time reranking, verification during RL training, and domain adaptation.

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