Another paper in the same vein (oscillators) but for classification and reasoning: https://t.co/P5mJy1hOFm
It seems that oscillators allows for good performance at much smaller parameter count. This is a super exciting line of work.
@mitsuhiko@badlogicgames Perhaps minimal clanked writing code is required and better global determinism as to how to attack problems is required.
Complexity used to be less complex. Now it’s slop above slop above slop. Too much.
How does self-regulated simulative reasoning perform in practice?
SR²AM-v0.1-8B achieves results competitive with GPT-OSS (120B) and GLM-4.6 (355B).
SR²AM-v1.0-30B is competitive with DeepSeek-V3.2 (685B) and Kimi-K2.5 (1T) at 𝟮𝟲–𝟵𝟱% fewer reasoning tokens than comparable 30/32B agentic LLMs.
The key finding from RL training: the model learns to plan further ahead (+22.8% horizon) rather than more often (+2% frequency). Allocation, not compression.