Fable Breaks Task Wall with Synthetic Reasoning Traces; GLM 5.3 Follows
mariofilhoml · x · 2026-08-23
User Mario cited ProximalHQ's findings on the Fable model's performance in the FrogsGame task, noting that GLM 5.3 and Grok 4.6 seem to have adopted a similar approach.
- Fable's Breakthrough: Using synthetic reasoning traces, Fable improved Qwen3-8B's solve rate on the FrogsGame post-training task from 3.8% to 67.8%, far surpassing Opus 4.8.
- Core Method: The solution relied on writing a backtracking solver and verbalizing the solver's actions into reasoning traces for training.
- Industry Trend: The user observes that GLM 5.3 and Grok 4.6 appear to have figured out the same technique to break through performance walls on this task.
More from Models
- Domain-specific post-training may beat general models: legal benchmark data revealed — kuza55 · 2026-08-23
- Flashback: GPT-4 cost $60/M output tokens with 8K context three years ago — gajesh · 2026-08-23
- Open Weights vs Frontier: Just a 3-Point Gap but 1/3 the Cost — MicahBerkley · 2026-08-23
- Ox Alpha Overhyped? Beats GPT-5.6-Luna but Lags Other Frontiers — Al_Grigor · 2026-08-23
- Dev Endorses k3 + NousResearch Harness as Best Combo — markjeffrey · 2026-08-23
- Ornith 1.5 35B Hits 81.8 on GPQA with Thinking Mode, Decodes at 303 tok/s — MikePFrank · 2026-08-23