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HFEPX · Eval paper review

CLARity: Reasoning Consistency Alone Can Teach Reinforced Experts

Jiuheng Lin, Cong Jiang, Zirui Wu, Jiarui Sun +1 more

Published

Oct 10, 2025

Citations

0

Trust level

Low

Usefulness score

37/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Domain Experts

Signals refreshed

Jun 28, 2026

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Tags

Automatic Metrics Coding Human Eval

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
37/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Training expert LLMs in domains with scarce data is difficult, often relying on multiple-choice questions (MCQs). However, standard outcome-based reinforcement learning (RL) on MCQs is risky. While it may improve accuracy, we observe it often degrades reasoning quality such as logical consistency. Existing solutions to supervise reasoning, such as large-scale Process Reward Models (PRMs), are prohibitively expensive. To address this, we propose CLARity, a cost-effective RL framework that enhances reasoning quality using only a small, general-purpose LLM. CLARity integrates a consistency-aware reward mechanism with a 2-stage refine-then-monitor training pipeline to enhance reasoning consistency, and a dynamic data reformulation strategy to to better exploit limited data. Experiments demonstrate that CLARity improves response consistency by 16.5% and accuracy by 7.5% over baselines. Human evaluations further confirm holistic improvements in coherence and professionalism. Thus, CLARity offers a generalizable solution that enables smaller models to effectively guide expert models by reasoning consistency. Our code is open sourced at: https://github.com/Infinite-set/CLARity

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Training expert LLMs in domains with scarce data is difficult, often relying on multiple-choice questions (MCQs)."

Evaluation Modes

partial

Human Eval, Automatic Metrics

Includes extracted eval setup.

"Training expert LLMs in domains with scarce data is difficult, often relying on multiple-choice questions (MCQs)."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Training expert LLMs in domains with scarce data is difficult, often relying on multiple-choice questions (MCQs)."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Training expert LLMs in domains with scarce data is difficult, often relying on multiple-choice questions (MCQs)."

Reported Metrics

partial

Accuracy, Coherence

Useful for evaluation criteria comparison.

"While it may improve accuracy, we observe it often degrades reasoning quality such as logical consistency."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"Training expert LLMs in domains with scarce data is difficult, often relying on multiple-choice questions (MCQs)."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

accuracycoherence
Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Domain Experts
Expertise required
Coding
Evaluation details
Evaluation modes
Human Eval, Automatic Metrics
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Training expert LLMs in domains with scarce data is difficult, often relying on multiple-choice questions (MCQs).

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Training expert LLMs in domains with scarce data is difficult, often relying on multiple-choice questions (MCQs).
  • However, standard outcome-based reinforcement learning (RL) on MCQs is risky.
  • While it may improve accuracy, we observe it often degrades reasoning quality such as logical consistency.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Human evaluation, Automatic metrics) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Expert verification evaluation Accuracy

Contribution summary

  • While it may improve accuracy, we observe it often degrades reasoning quality such as logical consistency.
  • To address this, we propose CLARity, a cost-effective RL framework that enhances reasoning quality using only a small, general-purpose LLM.
  • Human evaluations further confirm holistic improvements in coherence and professionalism.

Why it matters for eval

  • Human evaluations further confirm holistic improvements in coherence and professionalism.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    Detected: Human Eval, Automatic Metrics

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    Detected: accuracy, coherence