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RL Framework Boosts LLM Abductive Reasoning Across Diverse Tasks

Moein Salimi, Danial Parnian, Shaygan Adim, Amirmohammad Ebrahiminasab, Nima Alighardashi, Parsa Gholami, Sahand Akramipour, Mahdi Jafari Siavoshani, Mohammad Hossein Rohban· August 18, 2026 View original

Key takeaways

  • CEDAR-GRPO significantly enhances LLM abductive reasoning through process-aware RL.
  • Abductive rewards for evidence coverage and directionality are crucial for transferability.
  • Task diversity in training data improves generalization to unseen tasks.
  • This framework leads to more robust and intelligent AI systems for complex problem-solving.

Who benefits

Healthcare (diagnosis)Scientific ResearchCybersecurity (incident analysis)Legal (investigation)AI/ML Engineering

Summary

This paper introduces CEDAR-GRPO, a process-aware reinforcement learning framework that significantly improves large language models' abductive reasoning capabilities. By combining final-answer correctness with abductive rewards for evidence coverage and directionality, it enhances transferability across 11 unseen tasks, outperforming base models and correctness-only methods.

This research presents CEDAR-GRPO, a novel process-aware reinforcement learning (RL) framework designed to enhance the general abductive reasoning capabilities of large language models (LLMs). Abductive reasoning, which involves inferring the best explanation for observed phenomena, has typically been studied through narrow, task-specific benchmarks, limiting the transferability of improvements. CEDAR-GRPO aims to overcome this by focusing on transferable reasoning skills. The framework integrates rewards for both final-answer correctness and abductive process metrics, such as evidence coverage and the directionality of evidence-to-explanation links. Four open-weight LLMs were post-trained on a diverse, domain-neutral mix of abductive tasks and then evaluated on 11 entirely unseen tasks, including hypothesis selection, missing-fact generation, and clinical reasoning. CEDAR-GRPO consistently improved every model on every held-out task, demonstrating average gains of 7.4 points over base models and 2.7 points over correctness-only RL, confirming that RL, abductive reward design, and task diversity are crucial for transfer.

Why it matters

Improving LLMs' ability to perform general abductive reasoning is critical for developing more intelligent and robust AI systems capable of complex problem-solving, diagnosis, and scientific discovery across various domains.

How to implement this in your domain

  1. 1Explore integrating process-aware reinforcement learning techniques into LLM fine-tuning pipelines.
  2. 2Design custom reward functions that capture not just correctness but also reasoning process quality (e.g., evidence coverage).
  3. 3Develop diverse, domain-neutral datasets for training and evaluating abductive reasoning in LLMs.
  4. 4Apply enhanced abductive LLMs to tasks requiring complex explanation and diagnosis in specific domains.

Original post by Moein Salimi, Danial Parnian, Shaygan Adim, Amirmohammad Ebrahiminasab, Nima Alighardashi, Parsa Gholami, Sahand Akramipour, Mahdi Jafari Siavoshani, Mohammad Hossein Rohban

"arXiv:2608.14791v1 Announce Type: new Abstract: Abductive reasoning, often characterized as inference to the best explanation, is central to explanation under uncertainty, from everyday sense-making and investigation to scientific discovery. Yet LLM research has mostly studied ab…"

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Originally posted by Moein Salimi, Danial Parnian, Shaygan Adim, Amirmohammad Ebrahiminasab, Nima Alighardashi, Parsa Gholami, Sahand Akramipour, Mahdi Jafari Siavoshani, Mohammad Hossein Rohban on X · view source

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