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Self-Improvement with Auxiliary Rewards: Learning from Verified Traces and Evaluating Transfer — arXiv:2610.41296

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41296
License (as archived): CC0-1.0
Archived: 2026-11-02T06:59:05.379Z
SHA-256 of the archived text: 4de616b3efa819d605494fa22668a285eed9986fa5a0a24cc2a2c4fa02958005
Stable URL of this page: https://metraipolis.com/documento/dc_428fa02262db

How to cite

Self-Improvement with Auxiliary Rewards: Learning from Verified Traces and Evaluating Transfer — arXiv:2610.41296 — arXiv (en), https://arxiv.org/abs/2610.41296 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_428fa02262db (Metraipolis archive dc_428fa02262db, sha256 4de616b3efa819d605494fa22668a285eed9986fa5a0a24cc2a2c4fa02958005)

BibTeX

@misc{dc428fa02262db,
  title = {Self-Improvement with Auxiliary Rewards: Learning from Verified Traces and Evaluating Transfer — arXiv:2610.41296},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41296},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_428fa02262db; sha256 4de616b3efa819d605494fa22668a285eed9986fa5a0a24cc2a2c4fa02958005},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41296. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Answer-level rewards cannot distinguish reasoning traces with equally correct outcomes. We study whether auxiliary trajectory preferences improve small-language-model self-improvement and transfer under limited supervision. We compare confidence-based (SC) and entropy-based metrics (UID-Conj.), which combines global entropy variation and local smoothness, through online reward augmentation and offline training on correct responses. Online experiments adapt Qwen3-1.7B on 30 AIME problems without reference answers; offline experiments use historical AIME problems. UID-Conj. improves the online macro-average across the adaptation benchmark and three transfer benchmarks by 2.08 percentage points over unmodified TTRL. The strongest evidence comes from offline weighted SFT: UID-Conj. exceeds uniform weighting on the same response pool by 1.93 points, with a paired prompt-bootstrap 95\% CI of [0.44, 3.44]. Benefits are task-dependent: SC rewards judged trace quality and instruction following, while UID-Conj. rewards yield stronger training outcomes in several settings. We find that auxiliary preferences can expose learning value among equally correct traces not captured during inference time, motivating their evaluation through downstream gains.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.41296 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-02T06:56:58.286Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).