PostTrainBench v1.1 strengthens eval integrity and puts Fable 5 in the lead at 41.8%. Some reward hacks we fixed：
1/ Train-test contamination We re-audited historical runs under this policy and flagged 234 runs for train-test contamination.
Violations ranged from loading an entire evaluation set for memorization to generating synthetic templates around individual GSM8K and BFCL items. Runs that used observed failures to build genuinely diverse training data were retained.
Our rule： Agents may inspect benchmark failures and train broadly against the underlying failure mode. They may not generate training examples centered on particular test items， including paraphrases， variants， or shadow examples covering the same specific scenario.
2/ Submitting a different model 10 runs were flagged for model substitution.
Kimi K2.5 submitted the official Qwen3-1.7B instruct weights after its attempts to fine-tune Qwen3-1.7B-Base failed. The trace acknowledged the substitution and saved the replacement as final_model.
What we did： We added a programmatic model-identity check that compares the submitted artifact with reference configurations for known instruction-tuned models.
3/ Using external LLM APIs as teachers 12 runs were flagged for disallowed external API use.
Self-generation remains allowed. An agent can sample， filter， and retrain on outputs from the assigned model. What it cannot do is import the capability of a stronger external teacher. Loading models on the allocated compute remains allowed.
What we did： - separate API usage judge reviews tool calls and artifacts. - unrelated provider credentials are removed or blocked from the agent environment. - runs invalidated by external API use were rerun under the corrected setup.