RLVR bounds on Qwen3.5-4B land within 8–13% of training accuracy
ddkang · x · 2026-07-21
New RLVR generalization bounds on Qwen3.5-4B stay within 8–13% of training accuracy
Bridgewater AIA Labs, UIUC, and MIT report what they describe as the first non-vacuous generalization bounds for reasoning LLMs on real-world tasks.
- The framework targets RLVR and uses a compression-based PAC-Bayes approach.
- Because RLVR training is based on stochastic decoding, they first reparameterize decoding with the Gumbel-max trick.
- To make the bound non-vacuous, they introduce Progressive RLVR with:
- on-policy distillation
- TinyLoRA for extreme parameter efficiency
- quantization
In experiments on Qwen3.5-4B across Math, Code, General Knowledge, and Text-to-SQL, the bounds are:
- 8–13% away from the model’s training accuracy
- 17–51% above the base model’s accuracy
Their ablation shows each ingredient matters: removing distillation or training directly with TinyLoRA makes the bound looser, and replacing TinyLoRA with standard LoRA makes the bounds vacuous or meaningless on the smaller model.
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