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Soft-SVeRL: Self-Verified Reinforcement Learning with Soft Rewards research paper by Cohere, 2026

Cohere · Foundation models · May 27, 2026 · 1 citations

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What it shows

Turns each prompt into a checklist scored item by item, giving reinforcement learning partial-credit rewards for tasks that cannot be checked automatically.

By Saurabh Dash, Pierre Clavier, John Dang and 4 more · arXiv 2605.28561 · PDF

Summarised by hand from the abstract.

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All 30
ReasoningSep 9, 2026Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language ReasoningOptimized supervised fine-tuning data composition enables reasoning models to consistently process and respond in diverse non-English languages without requiring reasoning supervision in each target language.Cohere33 upvotesRead →ReasoningAug 18, 2026The IOL-AI Challenge: An Open Challenge towards Advancing Linguistic ReasoningAn open competition on unseen Linguistics Olympiad puzzles, graded by the official jury; small 14B systems beat models twice their size.Cohere1 citationsRead →ReasoningJun 23, 2026CALIBER: Calibrating Confidence Before and After Reasoning in Language ModelsTrains reasoning models to state their confidence twice, before thinking and after answering, halving calibration error against the best single-estimate method.Cohere1 citationsRead →Foundation modelsJun 22, 2026AI Exposure Scores: what they measure, what they miss, and what comes nextReviews how widely cited scores of which jobs AI can assist are used in policy, what they miss, and newer measures that address it.CohereRead →Alignment and safetyJun 11, 2026The Culture Funnel: You Can't Align What isn't in the DataModern LLM pipelines experience a cultural data funnel where explicit cultural signals diminish during post-training, necessitating shifts in training data approaches for better cultural alignment.Cohere1 citationsRead →Agents and evaluationApr 19, 2026Agents Explore but Agents Ignore: LLMs Lack Environmental CuriosityLLM-based agents fail to exploit discovered unexpected information despite recognizing it, indicating a lack of environmental curiosity that depends on tools, compute, and training data distribution.Cohere2 citations8 upvotesRead →Training and scalingMar 12, 2026Tiny Aya: Bridging Scale and Multilingual DepthTiny Aya demonstrates high-quality multilingual capabilities with 3.35 billion parameters through region-aware posttraining and balanced language performance.Cohere18 citations10 upvotesRead →Agents and evaluationFeb 27, 2026CIRCLE: A Framework for Evaluating AI from a Real-World LensA six-stage framework for measuring how deployed AI behaves and affects organisations, linking field tests and red teaming to usable metrics.Cohere1 citationsRead →
Topic
About this paper
Venue
arXiv.org
Citations
1, 0 influential · Semantic Scholar
Upvotes
- · Hugging Face
Lab
Cohere · on Companies · on Acquisitions · on Releases

Changes

CitationsSep 28, 2026A first count of 0 influential citations.
CitationsSep 28, 2026A first count of 1 citations.
New paperSep 26, 2026Added to the list.