arXiv:2511.07368v3 Announce Type: replace-cross Abstract: Foundation models exhibit broad knowledge but limited task-specific reasoning, motivating post-training strategies such as RL with verifiable rewards (RLVR) and test-time scaling (TTS). While recent work highlights the role of exploration in improving pass@K, empirical evidence points to a paradox: RLVR and ORM/PRM typically reinforce existing paths rather than expanding the reasoning scope, raising the question of why exploration helps if no new patterns emerge. To reconcile this paradox, we adopt the perspective of Kim et al. (2025),
This research emerges as post-training methods like RLVR become crucial for enhancing foundation models, yet their actual impact on reasoning diversity remains unclear.
Understanding distributional biases in post-training can significantly improve the efficacy of AI development, leading to more robust and genuinely intelligent models rather than those simply reinforcing existing patterns.
The analytical framework shifts from merely observing pass@K metrics to deeply investigating how reasoning trajectories are shaped and potentially limited by current post-training strategies.
- · AI researchers
- · Foundation model developers
- · AI safety and alignment researchers
- · Developers relying solely on brute-force exploration
- · Models exhibiting limited task-specific reasoning
Refined post-training strategies will emerge, focusing on true reasoning expansion.
More reliable and less biased AI models will be developed, improving real-world applications.
Increased trust in AI systems as their reasoning becomes more transparent and less prone to reinforcement of narrow paths.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI