Reasoning: From Reflection to Solution
Paper • 2511.11712 • Published • 2
Zixi "Oz" Li PRO
OzTianlu
AI & ML interests
My research focuses on deep reasoning with small language models, Transformer architecture innovation, and knowledge distillation for efficient alignment and transfer.
Recent Activity
reacted to reaperdoesntknow's post with 👍 about 12 hours ago
We present a methodology for training small language models on CPU at FP32 precision
that achieves capability-per-dollar efficiency orders of magnitude beyond GPU-based training.
Across15modelsspanningfournovelarchitecturefamilies—MixtureofAttentions(MoA),cross-
architecture fusion (Qemma), swarm intelligence (SAGI), and metric-space causal language
models (DiscoverLM)—total compute cost was $24 on a single AMD EPYC 9454P proces-
sor. We introduce seven methodological pillars: (1) FP32 precision preservation, with exper-
iments demonstrating 5,810×single-operation error and 23,225×compounding error ratio for
FP16 at network depth; (2) sparse cognitive architectures where 0.02–7% of parameters activate
per token, matching CPU branching rather than GPU SIMD; (3) developmental curriculum
training progressing from language to logic to transfer to depth; (4) continuous belt-fed data
ingestion eliminating truncation waste; (5) hardware-native optimization for AMD Zen 4 via
AOCL/OpenMP/NUMA-aware allocation; (6) self-regulating thermodynamic governance with
emergent temperature measurement grounded in L2-star discrepancy; and (7) open-standard
compute (AVX2 SIMD at FP32) free of proprietary vendor dependency. We argue that trans-
formers were designed for GPU hardware rather than mathematical optimality, and that archi-
tectures designed for geometric correctness—metric-space attention, triangle inequality enforce-
ment, sparse expert routing—naturally favor CPU execution. For sub-2B parameter models,
CPU training produces more capable models at a fraction of the cost. updated a model 3 days ago
NoesisLab/Kai-30B-Instruct reacted to danielhanchen's post with 🔥 14 days ago
We collaborated with NVIDIA to teach you about Reinforcement Learning and RL environments. 💚 Learn:
• Why RL environments matter + how to build them
• When RL is better than SFT
• GRPO and RL best practices
• How verifiable rewards and RLVR work
Blog: https://unsloth.ai/blog/rl-environmentsOrganizations
authored a
paper 2 months ago