Let's Think Through the Possibilities

TTP

Training language models to output calibrated distributions over candidate answers. These pages collect the question-answering results.

Pages

HotPotQA reasoning pairs
Side-by-side chains of thought of the standard ε = 5 × 10⁻⁵ cell and the reasoning-budget cell on 180 test units, with blind judge verdicts: where the budget cell notices missing evidence and hedges, and where it loops.
HotPotQA matching rules
Which strings count as the gold answer: exact match, token F1 and Zipf-weighted F1 measured on the 5,255 test questions, with the prior distribution under each rule and the wrongly placed mass estimated by an LM judge.
Campaign 4 HPQA
HotPotQA run of our framework at ε = 5 × 10⁻⁵, in the Campaign 4 QA layout: the 99 development units (33 questions × 0/1/2 gold paragraphs removed) across checkpoints, unit by unit, and the 417-step training stream with weight templates, trends and every group's rollouts.
Campaign 4 QA
ProtoQA parameter search of our framework: pick a cell (prior scale × ε) and follow it on the development set across checkpoints, question by question, and through its training stream beside the Campaign-4 Multi-RLCR rerun.
Campaign 3 QA
ProtoQA training dynamics of Campaign 3: the development set across checkpoints and the training stream beside Multi-RLCR. HotPotQA to follow.
Campaign 2 QA
Results tables, the ProtoQA frozen test set problem by problem with inspectable rollouts, and the ProtoQA training dynamics.
QA prompt analyses
Prompt comparisons for ProtoQA, HotPotQA and TriviaQA with Qwen3-8B, including the historical generation prompts.