CEDAR-GRPO: Process-Aware Reinforcement Learning for General Abductive Reasoning in LLMs

• URL: http://arxiv.org/abs/2608.14791v1
• Date: Fri, 14 Aug 2026 18:02:07 GMT
• Title: CEDAR-GRPO: Process-Aware Reinforcement Learning for General Abductive Reasoning in LLMs
• Abstract summary: We ask whether RL post-training can improve abduction as a transferable reasoning capability.<n>We introduce CEDAR- GRPO, a process-aware framework that combines final-answer correctness with abductive rewards for evidence coverage and evidence-to-explanation directionality.
• Score: 5.42362703313004
• License: http://creativecommons.org/licenses/by/4.0/
• 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 abduction through narrow, task-specific benchmarks, making it unclear whether observed gains transfer beyond the benchmark family used for training or evaluation. We ask whether RL post-training can improve abduction as a transferable reasoning capability. We introduce CEDAR-GRPO, a process-aware framework that combines final-answer correctness with abductive rewards for evidence coverage and evidence-to-explanation directionality. Four open-weight LLMs are post-trained on a controlled, domain-neutral mixture of abductive hypothesis-generation and hypothesis-selection tasks. We evaluate them on 11 unseen tasks spanning hypothesis selection, missing-fact generation, defeasible inference, long-context investigation, clinical reasoning, code debugging, and non-abductive controls. CEDAR- GRPO improves every model on every held-out task over both base models and correctness-only GRPO, with average gains of 7.4 and 2.7 points, respectively, and a maximum gain of 30.8 points. Ablations confirm that RL, abductive reward design, and task diversity each contribute to transfer. Process-level metrics further show stronger abductive behavior, including exploration of alternatives, elimination of rivals, backtracking, and uncertainty marking.

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