This article updates FrogsGame results using Claude Fable 5, Opus 4.8, and GPT-5.5 to see how AI agents have improved at post-training a base model. Fable 5 solved the key failure of low-quality SFT traces by programmatically generating correct reasoning with a backtracking algorithm, then fine-tuning and RL within the time budget, boosting pass@4 and calibration. It also improved time use, data diversity, curriculum strategies, and error recovery.

• Fable 5 solved the core failure of prior agents (training on bad self-generated traces) by using a backtracking algorithm to generate correct SFT data, then RL, all within the 20-hour budget.
• Fable 5's calibration was far better (1.2x overoptimism vs Opus 4.8's 4.9x) and it used the full time budget instead of quitting early.
• Opus 4.8 (30.9% pass@4) and GPT-5.5 (9.7%) cut obvious failure modes threefold but still didn't reliably beat the baseline; Claude skipped weak SFT entirely and went straight to GRPO, while GPT-5.5 still leaned on SFT and underused RL.
• Simple binary reward signals consistently outperformed more elaborate reward shaping across all models tested.