HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment

Entropy collapse in reinforcement learning for language models isn't just an abstract training instability: it means the model stops exploring alternative reasoning paths and converges prematurely on low-quality outputs, which is especially damaging when training examples are scarce. HEAL's core bet is that mixing in general-domain data can act as a kind of regularizer, keeping the policy distribution from collapsing before it has learned anything useful.

The gradient and collapse problems showing up here echo what we covered in IG-Search (arXiv, April 16), where researchers tackled gradient collapse in search-augmented reasoning by redesigning the reward signal at the step level rather than the trajectory level. Both papers are circling the same underlying tension: sparse or noisy reward signals in RL fine-tuning destabilize training in ways that standard supervised learning doesn't face. The rest of our recent archive skews toward inference efficiency and benchmarking, so HEAL sits more squarely in the training-stability thread than anything else we've published this week.

The meaningful test is whether HEAL's entropy alignment holds when scaled beyond the few-shot regime into standard data settings without degrading performance, since a fix that only works under scarcity has limited practical reach. Watch for follow-up ablations or reproductions on established math and code reasoning benchmarks in the next two to three months.