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Qwen3.5-0.8B

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RESEARCH · 1 SOURCE · arXiv cs.AI

Paper shows RLVR (with GRPO) works on Qwen3.5-0.8B without distillation

The arXiv preprint applies Reinforcement Learning with Verifiable Rewards (RLVR) using Group Relative Policy Optimization (GRPO) and an interleaved Wikipedia search tool to train Qwen3.5-0.8B on MuSiQue and a seven-benchmark QA suite. The best run reaches 0.352 average exact-match (vs 0.092 untrained), a 3.8× improvement with no distillation, and the authors find reward shape matters—sparse exact-match rewards perform worst for this model scale.

6.0

MODELS · 1 SOURCE · arXiv cs.AI

TinyCeNN-LM: quality-gated conversion framework replacing pretrained attention with CeNN-like recurrent layers

arXiv:2609.21139v1 introduces TinyCeNN-LM, a quality-gated post-training conversion framework that replaces attention layers with CeNN-inspired cellular-recurrent modules containing bounded local processing, compact recurrent memory, routing/fusion, and accept-or-rollback validation. The paper evaluates three implementations (Integrated Memory, MemoryFusion, PDelta3-GDN2-CLVR+Local32): on SmolLM2-135M layers 0–2 pass with cumulative ΔNLL=+0.01209 while layer 3 is rejected due to poor representation fidelity; on Qwen3.5-0.8B full-attention layers 3, 7, 11 are accepted with final ΔNLL=+0.02073; Integrated Memory keeps perplexity within −0.07% to +0.93% and can reduce total cache by up to 6.01%, and a 200-item downstream sanity check on converted Qwen releases yields 28.5%–32.0% accuracy.

6.0