When RL Suppresses Its Own Vocabulary: Recovering Reasoning Diversity in Puzzle-to-Math Transfer
arXiv:2605.29190v1 Announce Type: new Abstract: Reinforcement learning using verifiable rewards (RLVR) improves LLM reasoning, but the conditions under which it transfers across domains -- and why it does so -- remain under-explored. We study cross-domain transfer in a 7B model whose SFT and RL post-training stages use only constraint-satisfaction puzzles, with no mathematics problems in the post-training data. To analyze how transfer emerges, we introduce a reasoning primitive-level framework that combines a 9-class span classifier with motif extraction, allowing us to segment chain-of-though
This research is emerging as foundational models are increasingly applied to complex reasoning tasks, pushing the boundaries of their generalization capabilities beyond simple pattern matching.
Understanding how RL transfers reasoning across domains is crucial for developing more robust and generally intelligent AI, impacting future applications in varied fields without requiring domain-specific training.
The ability of AI models to apply reasoning learned in one domain to entirely different domains without explicit retraining could fundamentally alter model development and deployment paradigms.
- · AI developers
- · Reinforcement learning researchers
- · General AI applications
- · Problem-solving software
- · Narrower domain-specific AI solutions
- · Brute-force data labeling for new domains
RL-trained language models could exhibit more versatile and effective problem-solving in complex, previously unseen scenarios.
This improved versatility might lead to a significant acceleration in AI adoption across new sectors, reducing the need for extensive customized training data.
The development of truly 'reasoning' general-purpose AI could blur the lines between human and machine cognitive abilities in various intellectual tasks.
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Read at arXiv cs.LG