DeepSeek-R1-Distill-Qwen
deepseek-r1-distill-qwen-1a47d1b0·3 events·first seen 7d agoAliases: DeepSeek-R1-Distill-Qwen, DeepSeek-R1-Distill-Qwen-14B
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RACES framework enables recursive composition of verifiable RL environments for LLM reasoning generalization
RACES (Recursive Automated Composition for Environment Scaling) is a new framework that treats verifiable RL training environments as composable building blocks, automatically fusing them when input/output types match. The system implements 300 base environments and four composition operators (SEQUENTIAL, PARALLEL, SORT, SELECT) to generate diverse reasoning patterns at scale. Experiments show consistent gains on unseen benchmarks: DeepSeek-R1-Distill-Qwen-14B improves from 48.2 to 51.3 and Qwen3-14B from 58.8 to 61.1 averaged across six benchmarks. Notably, RACES achieves parity with 300 individual environments using only 50 base environments, suggesting strong efficiency gains over linear environment scaling.
N-GRPO: Semantic Neighbor Mixing for Improved Policy Optimization in LLM Reasoning
A new arXiv preprint introduces N-GRPO, an exploration strategy for the GRPO reinforcement learning framework that improves solution diversity during rollout by mixing embeddings of anchor tokens with their nearest semantic neighbors rather than using token-level sampling or random noise. The method is evaluated on DeepSeek-R1-Distill-Qwen models of various sizes and shows consistent improvements on math reasoning benchmarks plus out-of-distribution generalization. The work targets a known limitation in RLHF-style training: redundant rollout trajectories that reduce effective learning signal.
ReasonAlloc: Hierarchical KV Cache Budget Allocation for Long-CoT Reasoning Models
ReasonAlloc is a training-free framework that reframes decoding-time KV cache compression as a hierarchical budget allocation problem, operating at both layer-wise (offline) and head-wise (online) levels. The method identifies an architecture-driven pattern called the 'Reasoning Wave' to guide layer preallocation, then dynamically reallocates to information-rich heads during decoding. Evaluated on MATH-500 and AIME 2024 using DeepSeek-R1-Distill and AceReason models, it outperforms uniform-budget baselines (R-KV, SnapKV, Pyramid-RKV) especially at small budgets of 128–512 tokens, with negligible overhead.