Generality of mid-trace shift patterns in open-ended and multi-turn settings

Determine whether the empirical patterns observed for mid-trace reasoning shifts in reinforcement-learning–fine-tuned language models—specifically their rarity and typically negative impact on accuracy—also hold in open-ended reasoning tasks and in multi-turn interactions, where correctness is less constrained and dialogue dynamics may affect reasoning behavior.

Background

The paper analyzes mid-trace reasoning shifts (often signaled by cues like "wait" or "let’s reconsider") across three domains—cryptic crosswords, mathematical problem solving (MATH-500), and Rush Hour puzzles—using GRPO-fine-tuned Qwen and Llama models. Across 1M+ traces and multiple checkpoints and temperatures, the authors find that such shifts are rare and generally not beneficial to accuracy, with formal "Aha!" moments being extremely infrequent.

However, these domains have clear correctness criteria and largely single-turn, structured outputs. The authors note that it remains unknown whether the same patterns would persist in open-ended reasoning tasks or multi-turn interactions, where ambiguity, conversational context, and iterative refinement might change the role and utility of mid-trace shifts. This uncertainty motivates further investigation beyond the constrained settings studied.