Breaking Barriers: Do Reinforcement Post Training Gains Transfer To Unseen Domains? (2506.19733v1)

Abstract: Reinforcement post training (RPT) has recently shown promise in improving the reasoning abilities of 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.

• The paper demonstrates that reinforcement post training significantly enhances structured reasoning, especially in tasks like mathematics and coding.
• The observational study reports an average 3.57% improvement in-domain contrasted with a 1.48% decrease out-of-domain.
• The interventional study confirms that cross-domain transfer remains limited, indicating a need for novel training strategies.

Insights into Domain Generalizability of Reinforcement Post Training in LLMs

The paper "Breaking Barriers: Do Reinforcement Post Training Gains Transfer To Unseen Domains?" explores the generalizability of Reinforcement Post Training (RPT) in LLMs across various domains. This investigation is essential for understanding the extent to which RPT enhances a model's reasoning abilities beyond its training data. The results of the authors' studies indicate a nuanced impact of RPT, highlighting limitations in its cross-domain generalization capability.

Overview of Studies and Findings

The authors conducted two complementary studies—a broad observational paper and a controlled interventional paper. The observational paper evaluated 14 RPT-enhanced models against their base counterparts across multiple benchmarks, both within their training (in-domain, ID) and outside their training (out-of-domain, OOD) domains. Conversely, the interventional paper was designed to eliminate confounding factors by fine-tuning LLMs on single-domain data and then evaluating them across multiple domains.

Observational Findings

The observational paper revealed that models fine-tuned on structured reasoning domains, such as mathematics and programming, exhibited significant improvements in analogous tasks but struggled to maintain these gains in differing unstructured domains like legal or medical reasoning. The results indicated a consistent pattern where models achieved higher pass@1 scores within the domains they were specifically fine-tuned on, confirming an inherent advantage of RPT in relevant domains but showing negligible transfer to OOD tasks. The average improvement on ID tasks was 3.57%, contrasting with a 1.48% decrease outside of those domains.

Interventional Findings

The interventional setup provided a clearer lens, showing that models trained on singular domains gained significantly within their respective training domains yet displayed minimal to no statistically significant advantages when applied to OOD tasks. For example, models fine-tuned on mathematics showed appreciable transfer to coding-related tasks, indicating a degree of shared reasoning patterns between these structured domains. However, this transferability did not extend to unstructured domains, underscoring a fundamental limitation of RPT in promoting broad reasoning versatility in LLMs.

Implications and Future Directions

These findings suggest essential implications for the application and development of reinforcement learning techniques in LLMs. While RPT can significantly enhance model reasoning in well-defined domains, its inability to generalize across domains with differing reasoning requirements poses a critical challenge. This specificity indicates the need for improving RPT frameworks or exploring new training paradigms that can promote cross-domain generalizability.

The results demonstrated that unstructured domain reasoning patterns might implicitly encompass structured reasoning elements, but not vice versa. This insight could guide the development of future LLMs that more effectively integrate wide-ranging reasoning capabilities. It might involve designing RPT or similar reinforcement learning methods to incorporate more diverse data types or alternative methods like curriculum learning to better capture cross-domain task dependencies.

Conclusion

The paper provides a pivotal understanding of the limitations inherent in current RPT approaches, emphasizing that while RPT is advantageous for specific structured reasoning tasks, its generalization is restricted when applied to domains requiring fundamentally different reasoning approaches. Future research could focus on mitigating these limitations, exploring more holistic training methods, or designing models capable of adaptive reasoning across varied knowledge domains. The extension of this work can significantly impact the development of LLMs suited for tasks that necessitate robust and versatile reasoning, particularly those mirroring complex real-world scenarios.