Self-Improvement with Auxiliary Rewards: Learning from Verified Traces and Evaluating Transfer
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabThe paper studies whether auxiliary trajectory preferences improve self-improvement in small language models under limited supervision. It compares confidence-based (SC) and entropy-based (UID-Conj.) metrics via online reward augmentation and offline training on correct responses. Online experiments adapt Qwen3-1.7B on 30 AIME problems without reference answers. UID-Conj. improves the online macro-average by 2.08 points over unmodified TTRL, and offline weighted SFT shows a 1.93-point gain over uniform weighting.
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- Primary sourceHugging Face Daily Papers
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The paper studies whether auxiliary trajectory preferences improve self-improvement in small language models under limited supervision. It compares confidence-based (SC) and entropy-based (UID-Conj.) metrics via online reward augmentation and offline training on correct responses. Online experiments adapt Qwen3-1.7B on 30 AIME problems without reference answers. UID-Conj. improves the online macro-average by 2.08 points over unmodified TTRL, and offline weighted SFT shows a 1.93-point gain over uniform weighting.
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- 04What could mislead?
Experiments use a single small model (Qwen3-1.7B) and a limited set of AIME-based problems; source claims not independently checked.
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Key findings from the stored brief
- UID-Conj. improves the online macro-average across the adaptation and three transfer benchmarks by 2.08 points over unmodified TTRL.
- In offline weighted SFT, UID-Conj. exceeds uniform weighting on the same response pool by 1.93 points.
- The reported paired prompt-bootstrap 95% CI for the offline gain is [0.44, 3.44].
- Benefits are task-dependent: SC rewards judged trace quality and instruction following, while UID-Conj. yields stronger training outcomes in several settings.
Experiments use a single small model (Qwen3-1.7B) and a limited set of AIME-based problems; source claims not independently checked.
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Why this may matter
Answer-level rewards cannot separate reasoning traces that reach the same correct answer. The work suggests auxiliary preferences can reveal learning value among such traces, a signal that inference-time evaluation misses, and that transfer to downstream tasks is a useful way to judge these metrics.
Limits and open questions
Experiments use a single small model (Qwen3-1.7B) and a limited set of AIME-based problems; source claims not independently checked.
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