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Breaking Barriers: Do Reinforcement Fine-tuning Gains Transfer To Unseen Domains?

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
large language modelsreinforcement learningsupervised fine-tuninggeneralizability
Abstract:

Reinforcement post training (RPT) has recently shown promise in improving the reasoning abilities of large language models (LLMs). However, it remains unclear how well these improvements generalize to new domains, as prior work evaluates RPT models on data from the same domains used for fine-tuning. To understand the generalizability of RPT, we conduct two studies. (1) Observational: we compare a wide range of open-weight RPT models against their corresponding base models across multiple domains, including both seen and unseen domains in their fine-tuning data. (2) Interventional: we fine-tune LLMs with RPT on single domains and evaluate their performance across multiple domains. Both studies converge on the same conclusion that, although RPT brings substantial gains on tasks similar to the fine-tuning data, the gains generalize inconsistently and can vanish on domains with different reasoning patterns.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper investigates whether reinforcement post-training (RPT) improvements in large language models generalize across reasoning domains through both observational comparisons of existing RPT models and controlled interventional experiments. It resides in the 'Observational and Interventional Transfer Analysis' leaf, which contains only two papers total, indicating a relatively sparse research direction within the broader taxonomy. This leaf sits under 'Empirical Studies of Cross-Domain Transfer in Reinforcement Post-Training', suggesting the work addresses a fundamental but underexplored question about RPT's domain-general capabilities.

The taxonomy reveals neighboring research directions that contextualize this work's position. Sibling leaves include 'Comparative Analysis of SFT versus RL Generalization' (examining memorization versus reasoning), 'Boundary Probing of RLVR Reasoning Capabilities' (systematic capability assessment), and 'Math-to-General Reasoning Transfer Assessment' (specific transfer pathways). The parent branch 'Empirical Studies of Cross-Domain Transfer' excludes theoretical frameworks and method development, distinguishing this empirical investigation from the 'Reinforcement Learning Frameworks for Multi-Domain Reasoning' branch, which develops unified architectures rather than analyzing existing models' transfer properties.

Among thirty candidates examined across three contributions, none yielded clear refutations. The observational study (ten candidates, zero refutable) and interventional study (ten candidates, zero refutable) both appear novel within the limited search scope. The unified multi-domain evaluation framework (ten candidates, zero refutable) similarly shows no substantial prior overlap among examined papers. Given the sparse two-paper leaf and absence of refuting candidates in this top-thirty semantic search, the work appears to occupy relatively unexplored territory, though the limited search scale means potentially relevant work outside these candidates cannot be ruled out.

The analysis suggests the paper addresses a gap in understanding RPT generalization mechanisms, particularly through its dual observational-interventional design. However, the assessment is constrained by examining only thirty semantically similar candidates and does not cover the full breadth of RL reasoning literature. The sparse taxonomy leaf and zero refutations among examined candidates indicate novelty within the analyzed scope, though exhaustive coverage of related transfer learning or domain adaptation work remains beyond this analysis.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Generalizability of reinforcement post-training across reasoning domains. The field examines whether reinforcement learning methods that improve reasoning in one domain (e.g., mathematics) can transfer effectively to others (e.g., coding, commonsense reasoning, or vision-language tasks). The taxonomy reveals several complementary perspectives: empirical studies of cross-domain transfer probe how well RL-trained models generalize beyond their training distribution, often through observational or interventional analyses; reinforcement learning frameworks for multi-domain reasoning develop unified architectures or training recipes that span multiple problem types; domain-specific applications demonstrate RL successes in areas like medicine (Med R1[9]), logic (Logic RL[12]), or vision-language reasoning (Reason RFT Visual[1]); training methodologies and optimization branches explore algorithmic innovations such as curriculum learning or contrastive objectives; generalization in RL environments investigates procedural generation and benchmark design to stress-test transfer; and surveys (RL Survey Reasoning[2], Post Training Survey[47]) synthesize methodological insights across these threads. A particularly active line of work focuses on whether post-training with RL on one reasoning task yields models that perform well on held-out domains without further fine-tuning. Breaking Barriers[0] sits squarely within the empirical studies of cross-domain transfer, specifically under observational and interventional transfer analysis, examining how RL improvements in a source domain manifest in target domains. This contrasts with domain-specific efforts like Med R1[9] or EHR Reasoning RL[24], which tailor RL to specialized contexts, and with multi-domain frameworks such as Multi Domain RL[27] or Cross Domain RL[22], which explicitly train across several domains simultaneously. A closely related work, Breaking Barriers Post[6], likely explores similar transfer questions but may differ in experimental scope or intervention design. The central open question remains whether RL post-training induces domain-general reasoning capabilities or primarily overfits to the training domain's surface patterns, a tension that SFT Memorizes RL[5] and RL Incentivize Reasoning[3] also address from complementary angles.

Claimed Contributions

Observational study of RPT model generalizability across domains

The authors conduct a systematic observational study evaluating 14 open-weight reinforcement post-training models and their base models across 16 benchmarks spanning math, code, and knowledge-intensive reasoning domains. This study assesses how well RPT improvements transfer from seen to unseen domains.

10 retrieved papers
Interventional study isolating single-domain RPT effects

The authors design and execute a controlled interventional study where they fine-tune models using RPT on three disjoint single-domain datasets (math, code, knowledge-intensive reasoning) with consistent configurations. This isolates the effect of RPT from confounding factors like mixed-domain training data.

10 retrieved papers
Unified multi-domain evaluation framework for RPT generalizability

The authors propose a systematic two-stage pipeline combining observational and interventional studies with a unified evaluation framework across 16 benchmarks. This framework enables rigorous assessment of RPT generalizability across structured and unstructured reasoning domains.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Observational study of RPT model generalizability across domains

The authors conduct a systematic observational study evaluating 14 open-weight reinforcement post-training models and their base models across 16 benchmarks spanning math, code, and knowledge-intensive reasoning domains. This study assesses how well RPT improvements transfer from seen to unseen domains.

Contribution

Interventional study isolating single-domain RPT effects

The authors design and execute a controlled interventional study where they fine-tune models using RPT on three disjoint single-domain datasets (math, code, knowledge-intensive reasoning) with consistent configurations. This isolates the effect of RPT from confounding factors like mixed-domain training data.

Contribution

Unified multi-domain evaluation framework for RPT generalizability

The authors propose a systematic two-stage pipeline combining observational and interventional studies with a unified evaluation framework across 16 benchmarks. This framework enables rigorous assessment of RPT generalizability across structured and unstructured reasoning domains.

Table of Contents