Action Elimination through Hallucination Detection: A Reinforcement Learning Approach for LLM Fine-Tuning
Why this work is in the frame
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Bibliographic record
Abstract
The present paper proposes a novel fine-tuning framework for large language models (LLMs) in unlabeled data settings by integrating reinforcement learning (RL) with an action elimination mechanism. The proposed method is designed to detect and filter out responses that are prone to hallucinations prior to policy updates, thereby enabling RL to focus exclusively on high-quality outputs. This has been demonstrated to reduce noise during training, improve reward signal quality, and accelerate convergence. Experiments conducted on mathematics and reasoning benchmarks demonstrate consistent performance improvements across multiple model architectures. In particular, the application of action elimination has been demonstrated to engender supplementary gains in both domain-specific and cross-domain reasoning tasks. The findings of this study suggest that substantial performance enhancements can be attained in the absence of labelled data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it