Tiny LLMs with self-reflection just beat a 100B teacher on key tasks, flipping the bigger-is-better script. Two posts show Qwen2.5-3B and Qwen2.5-1.5B outperform Claude 3.5 Haiku (about 100B) with 86% vs 81% on the benchmark. Training costs reportedly stay under $10, and the work is fully open source [1].
How it works - Step 1: Teacher Self-Improvement ("Linguistic RL"): Claude solves a problem, is told if correct, then reflects. A journal line like "I need to check ALL overlaps" helps drive accuracy from 81% to 84% [1]. - Step 2: Extract Strategy: The learned reasoning becomes a natural-language curriculum [1]. - Step 3: Train Student with LoRA: Fine-tune small models on examples showing the problem, Claude's strategy, and the answer; the Qwen2.5-3B learns an O(n log n) sweep-line algorithm and reaches 96% on easy problems [1].
Why it matters - Economics: Training costs < $10 in API calls; inference runs for free locally; 100–1000× cheaper than API deployment [1]. - Science: 67× compression (100B → 1.5B) with a performance gain; learned algorithmic reasoning rather than pure pattern matching [1]. - Safety: Human-readable learning traces; auditable; no black-box distillation [1]. - Democratization: Frontier capabilities on consumer hardware; one-time extraction; fully open source [1].
Code & reproducibility: Zenodo published DOI 17585532; GitHub repo with fixed seeds and logs [1]. ✅ Also echoed in a second post [2].
Bottom line: smaller, self-reflective LLMs are reshaping benchmarks and open-source AI progress. Watch if broader tasks confirm the trend [2].
References
Linguistic RL: 3B Models Exceed 100B Performance (86% vs. 81%)
Tiny 3B/1.5B models beat 100B teacher via self-reflection and policy extraction, enabling cheap, open-source LLM compression and replication
View source[D] Linguistic RL: 3B Models Exceed 100B Performance Through Self-Reflection (86% vs 81%)
Tiny 3B/1.5B models beat Claude-3.5 Haiku 100B via self-reflection, learning strategy, LoRA, open-source, cheap, reproducible, auditable, multi-domain potential for LLMs.
View source