arXiv:2606.28166v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has significantly improved the reasoning capability of large language models, reaching expert or even superhuman performance in domains such as competition math. However, whether weaker agents and humans can actually harness this capability is far less certain, with RLVR documented to drift reasoning toward idiosyncratic patterns such as poor readability and language mixing. Tandem training is a recently introduced paradigm that targets this compatibility problem: a trained, stronger senior co
The rapid advancement of large language models (LLMs) through techniques like RLVR necessitates addressing the usability gap for broader adoption beyond expert users.
This research could democratize access to advanced AI capabilities by making sophisticated LLM reasoning understandable and usable by a wider range of agents and humans.
The focus is shifting towards making powerful AI outputs more compatible with human understanding and weaker AI agents, rather than solely maximizing performance metrics.
- · AI developers focused on explainability
- · Enterprises deploying advanced LLMs
- · Non-expert users of AI systems
- · AI models with idiosyncratic or uninterpretable outputs
- · Specialized AI domains requiring high human readability
Improved human-AI collaboration and adoption of advanced AI in more diverse settings.
Reduced barriers for integrating powerful LLMs into varied applications, potentially accelerating automation across industries.
Enhanced trust and reliability in AI systems due to verifiable and understandable reasoning, leading to broader societal acceptance.
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Read at arXiv cs.AI