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home / papers / 2026 / ARF-RLHF: Adaptive Reward-Following for RLHF through Emotion-Driven Self-Supervision and Trace-Biased Dynamic Optimization

ARF-RLHF: Adaptive Reward-Following for RLHF through Emotion-Driven Self-Supervision and Trace-Biased Dynamic Optimization

Paper · 2026 · Yuxuan Zhang

Summary

Standard RLHF methods such as PPO and DPO depend on pairwise binary preference labels. These labels are expensive to collect, coarse, and skewed toward group-level tastes rather than individual users. This paper proposes Adaptive Reward-Following (ARF), a self-supervised pipeline that draws graded satisfaction signals from users' free-form follow-up messages, motivated by psycholinguistic evidence that people express approval and displeasure in consistent ways. The pipeline has four steps:

  • A Static Satisfaction Scorer reads a user's reply and estimates a negative/neutral/positive distribution for the previous model response.
  • These estimates are expanded in an Augmentation Database through synonym swaps, truncation and preference-weighted rebalancing.
  • The expanded data, used as soft labels, trains an adaptive ARF Scorer that is periodically updated.
  • The ARF Scorer's continuous rewards drive a new fine-tuning algorithm, TraceBias, which optimizes reward trajectories rather than binary comparisons.

Across several small LLM families and four preference domains, ARF beats PPO by 3.3% and DPO by 7.6%. The work suggests a cheaper route to personalized alignment with less manual annotation.

Key points

  • Both scorers are built on RoBERTa-base (called "mini" in the text). The adaptive scorer uses Experience Replay over DailyDialog and GoEmotions to limit forgetting.
  • TraceBias with DAM showed lower variance and steadier gradient norms than PPO at clip ε = 0.2. The author also gives a proof that TraceBias is consistent with standard RLHF.
  • The static scorer reaches only about 70% accuracy. Neutral-dominated predictions are therefore not used as direct reward signal in TraceBias updates; they shrink the update instead.
  • Evaluation covered Qwen-2/2.5, Gemma-2 and Llama-3.2 at 1.5B–3B parameters. All methods were judged against a fixed, held-out scorer, with additional checks by DeepSeek-V3 and GPT-5.
  • Ablations show that rescoring is needed to follow preference reversals on ALPACA and GSM8K. The author acknowledges that no real human evaluation was done.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of AberdeenAberdeen · United Kingdom23 works · 1,583 citations🎓South China Normal UniversityGuangzhou · China8 works · 24 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 works

🧭 Topics 1Research subjects and languages the work is about.Sentiment & emotion · 1,035 works

Cite

Cite as Yuxuan Zhang. 2026. ARF-RLHF: Adaptive Reward-Following for RLHF through Emotion-Driven Self-Supervision and Trace-Biased Dynamic Optimization. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1637

@inproceedings{zhang2026arf,
  title = {{ARF-RLHF: Adaptive Reward-Following for RLHF through Emotion-Driven Self-Supervision and Trace-Biased Dynamic Optimization}},
  author = {Yuxuan Zhang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1637},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1637}
}

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