Parallel Scaling Law: Unveiling Reasoning Generalization through A
  Cross-Linguistic Perspective
Recent advancements in Reinforcement Post-Training (RPT) have significantly
enhanced the capabilities of Large Reasoning Models (LRMs), sparking increased
interest in the generalization of RL-based reasoning. While existing work has
primarily focused on investigating its generalization across tasks or
modalities, this study proposes a novel cross-linguistic perspective to
investigate reasoning generalization. This raises a crucial question:
Does the reasoning capability achieved from English RPT effectively
transfer to other languages? We address this by systematically evaluating
English-centric LRMs on multilingual reasoning benchmarks and introducing a
metric to quantify cross-lingual transferability. Our findings reveal that
cross-lingual transferability varies significantly across initial model, target
language, and training paradigm. Through interventional studies, we find that
models with stronger initial English capabilities tend to over-rely on
English-specific patterns, leading to diminished cross-lingual generalization.
To address this, we conduct a thorough parallel training study. Experimental
results yield three key findings: First-Parallel Leap, a substantial
leap in performance when transitioning from monolingual to just a single
parallel language, and a predictable Parallel Scaling Law, revealing
that cross-lingual reasoning transfer follows a power-law with the number of
training parallel languages. Moreover, we identify the discrepancy between
actual monolingual performance and the power-law prediction as
Monolingual Generalization Gap, indicating that English-centric LRMs
fail to fully generalize across languages. Our study challenges the assumption
that LRM reasoning mirrors human cognition, providing critical insights for the
development of more language-agnostic LRMs.
68df3a3bdf49fb0df1e03cbd
Wen Yang
68df3a3bdf49fb0df1e03cbe
Junhong Wu
68df3a3bdf49fb0df1e03cbf
Chong Li
68df3a3bdf49fb0df1e03cc0
Chengqing Zong
68df3a3bdf49fb0df1e03cc1
Jiajun Zhang
Parallel Scaling Law: Unveiling Reasoning Generalization through A
  Cross-Linguistic Perspective
Recent advancements in Reinforcement Post-Training (RPT) have significantly
enhanced the capabilities of Large Reasoning Models (LRMs), sparking increased
interest in the generalization of RL-based reasoning. While existing work has
primarily focused on investigating its generalization across tasks or
modalities, this study proposes a novel cross-linguistic perspective to
investigate reasoning generalization. This raises a crucial question:
Does the reasoning capability achieved from English RPT effectively
transfer to other languages? We address this by systematically evaluating
English-centric LRMs on multilingual reasoning benchmarks and introducing a
metric to quantify cross-lingual transferability. Our findings reveal that
cross-lingual transferability varies significantly across initial model, target
language, and training paradigm. Through interventional studies, we find that
models with stronger initial English capabilities tend to over-rely on
English-specific patterns, leading to diminished cross-lingual generalization.
To address this, we conduct a thorough parallel training study. Experimental
results yield three key findings: First-Parallel Leap, a substantial
leap in performance when transitioning from monolingual to just a single
parallel language, and a predictable Parallel Scaling Law, revealing
that cross-lingual reasoning transfer follows a power-law with the number of
training parallel languages. Moreover, we identify the discrepancy between
actual monolingual performance and the power-law prediction as
Monolingual Generalization Gap, indicating that English-centric LRMs
fail to fully generalize across languages. Our study challenges the assumption
that LRM reasoning mirrors human cognition, providing critical insights for the
development of more language-agnostic LRMs.
Research investigates cross-linguistic transferability of reasoning capabilities in Large Reasoning Models, revealing significant variations and proposing a parallel training approach to improve generalization across languages.
Reinforcement Post-Training Large Reasoning Models cross-linguistic perspective multilingual reasoning benchmarks cross-lingual transferability interventional studies parallel training Parallel Leap Parallel Scaling Law Monolingual Generalization Gap