Post-Reasoning boosts LLM accuracy by reversing the usual answer-after-reasoning order, delivering mean relative gains of 17.37% across 117 model-benchmark pairs with zero extra cost.
Verithinker: Learning to verify makes reasoning model efficient
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BET reduces reasoning tokens by about 55% on average while improving performance across benchmarks by learning to short-solve easy queries, fold early on unsolvable ones, and preserve budget for hard solvable queries.
CoT compression frequently introduces trustworthiness regressions with method-specific degradation profiles; a proposed normalized efficiency score and alignment-aware DPO variant reduce length by 19.3% with smaller trustworthiness loss.
A six-dimensional MathVerifier supplies hard negatives and per-sample weights that improve DPO performance on math reasoning for a 1.5B Qwen2.5 model over standard SFT and unweighted DPO.
A survey organizing techniques to achieve efficient reasoning in LLMs by shortening chain-of-thought outputs.
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Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost
Post-Reasoning boosts LLM accuracy by reversing the usual answer-after-reasoning order, delivering mean relative gains of 17.37% across 117 model-benchmark pairs with zero extra cost.