ShortReasoning

In ShortReasoning, I show that rewriting a model’s earlier reasoning into compact state can shorten its newly generated reasoning without retraining.

In the coding-agent case study, MiniMax M3 with external compaction cost 34% less and used 59% less summed model-request time, including rewrite overhead.
(all “frontier” open models from july 2026 were tested - DeepSeek, Nemotron, GLM, Kimi, MiniMax and all on average produce shorter reasoning)

I built a simple compactor, but there is plenty anyone can play with:

• Which model compacts: a specialist fine-tune, a smaller cheaper model, or a fast “System 1”-style model. Classifiers could help select what to keep.
• What context it gets: one reasoning block, or more of the task and history?
• How much it compacts: individual blocks, many-to-many rewrites, or many-to-one summaries.
• How faithfully: aim to preserve information, as I did, or summarize only the most important parts.
• How inference is scheduled: run compression asynchronously alongside tool execution to potentially hide some rewrite latency.

Broader takeaway

Across all six tested routes, accepted compact histories led to shorter median continuations. My bet is that the capability is already there across most, perhaps all, current open-weight reasoning models. We should incentivize future generations to use it, expressing useful analysis in fewer tokens while keeping their problem-solving ability.

This method can already generate compact traces for simple self-distillation. The compressor can be the model itself, or potentially a weaker model, since rewriting may be easier than solving the original task.