# Optimizing Long-Form Clinical Text Generation with Claim-Based Rewards

arXiv: https://arxiv.org/abs/2510.02338 · HTML: https://mlsanity.com/paper/2510.02338/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Medical LLM alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Medical LLM alignment](https://mlsanity.com/alignment/medical-llm-alignment/)

## Builds on (most cited first, 13 of 13)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation](https://mlsanity.com/paper/2306.02022/) (2023)
- [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024)
- [NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes](https://mlsanity.com/paper/2310.15959/) (2023)
- [Comparing Two Model Designs for Clinical Note Generation; Is an LLM a Useful Evaluator of Consistency?](https://mlsanity.com/paper/2404.06503/) (2024)
- [Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation](https://mlsanity.com/paper/2405.18346/) (2024)
- [Abstractive Text Summarization: State of the Art, Challenges, and Improvements](https://mlsanity.com/paper/2409.02413/) (2024)
- [DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation](https://mlsanity.com/paper/2311.09581/) (2023)
- [Towards Adapting Open-Source Large Language Models for Expert-Level Clinical Note Generation](https://mlsanity.com/paper/2405.00715/) (2024)
- [Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models](https://mlsanity.com/paper/2305.05982/) (2023)
- [Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning](https://mlsanity.com/paper/2403.13089/) (2024)

## Built on by (most cited first, 3 of 3)

- [The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy](https://mlsanity.com/paper/2607.11175/) (2026)
- [Knowledge Graph-Augmented Ambient AI for Clinical Note Generation](https://mlsanity.com/paper/2609.22239/) (2026)
- [ASCRIBE: Atomic and Significance-Based Reasoning for Thai Clinical SOAP Note Generation](https://mlsanity.com/paper/2610.01234/) (2026)

## Abstract (arXiv)

Automating clinical documentation with large language models requires precise alignment with priorities such as completeness and factual grounding. We present an evaluation-integrated reinforcement learning framework for long-form clinical text generation that couples Group Relative Policy Optimization (GRPO) with DocLens, a claim-level evaluator that provides deterministic, dialogue-grounded rewards. Our method directly optimizes factual grounding and completeness without training a separate reward model or relying on human-authored references. Empirically, the approach improves clinical note quality and reduces training cost via a simple reward-gating strategy. An independent GPT-5 qualitative evaluation further supports these gains, showing higher preference for GRPO outputs in factuality, completeness, and brevity, with fewer omissions and hallucinations. Because the benchmarks are relatively clean and the base model already well aligned, these improvements likely represent a conservative lower bound. The framework is scalable to real-world settings and can incorporate custom objectives such as guideline adherence or billing preferences.