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.
More from Research
- Causal-only attention for non-generative tasks is wasteful, argues HF engineer — antoine_chaffin · 2026-09-11
- Catholic University of Chile researcher: scaling AI feedback is key to sustainable medical education — julianvarascom · 2026-09-11
- Nature paper images cellular activity across all organs, revealing body-wide circuits — arjunrajlab · 2026-09-11
- SignNet 1M Dataset Released for Sign Language Research — ducha_aiki · 2026-09-11
- ECCV26 Oral: Flow Matching Enables Single-Stage Multi-View Point Cloud Registration — ducha_aiki · 2026-09-11
- InFlux++ Method Released — ducha_aiki · 2026-09-11