Hugging Face Trending Papers
Jun 3
GRAIL: Gradient-Reweighted Advantages for Reinforcement Learning with Verifiable Rewards
Reinforcement learning with verifiable rewards (e. g.
Expected Value Alignment for Generative Reward Modeling in Formal Mathematics Verification
arXiv:2606. 01160v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used with formal interactive theorem provers such as Lean 4.
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arXiv AI
Jun 19
Process-Verified Reinforcement Learning for Theorem Proving via Lean
arXiv:2606. 20068v1 Announce Type: new Abstract: While reinforcement learning from verifiable rewards (RLVR) typically has relied on a single binary verification signal, symbolic proof assistants in formal reasoning offer rich, fine-grained structured feedback.
arXiv Machine Learning
Jun 3
Right Makes Might: Aligning Verified Hidden States Empowers RL Reasoning
arXiv:2606. 03234v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has become the dominant approach for improving mathematical reasoning in large language models, yet current methods reduce each correct rollout to a single reward bit, ignoring the geometric structure shared among their hidden states.
arXiv AI
Jul 15
Rethinking Reward Models for Multi-Domain Test-Time Scaling
arXiv:2510. 00492v3 Announce Type: replace Abstract: The reliability of large language models (LLMs) during test-time scaling is often assessed with \emph{external verifiers} or \emph{reward models} that distinguish correct reasoning from flawed logic.
arXiv AI
Aug 12
Evaluation-Conditioned Training: Teaching Models to Generalize to Stronger Oversight Regimes
arXiv:2608. 10209v1 Announce Type: new Abstract: Feedback signals used to train Large Language Models (LLMs) are the primary driver of their behavior and our main lever for instilling alignment with human values and objectives.
arXiv AI
Jun 2
Reward Score Matching: Unifying Reward-based Fine-tuning for Flow and Diffusion Models
arXiv:2604. 17415v3 Announce Type: replace-cross Abstract: Reward-based fine-tuning steers a pretrained diffusion or flow-based generative model toward higher-reward samples while remaining close to the pretrained model.
arXiv AI
Jun 11
PRInTS: Reward Modeling for Long-Horizon Information Seeking
arXiv:2511. 19314v2 Announce Type: replace Abstract: Information-seeking is a core capability for AI agents, requiring them to gather and reason over tool-generated information across long trajectories.
arXiv Machine Learning
Jun 9
Adaptive Generate-Rank-Verify: Inference-Time Search with Costly Verification
arXiv:2605. 17609v2 Announce Type: replace Abstract: Many inference-time language-model pipelines combine a cheap reward signal with an expensive verifier, such as exact answer checking in mathematical reasoning or hidden-test execution in code generation.
arXiv AI
Jul 2
Right in the Right Way: LM Training with Verifiable Rewards and Human Demonstrations
arXiv:2607. 01181v1 Announce Type: cross Abstract: RL with verifiable rewards (RLVR) has emerged as a powerful paradigm for training LMs on tasks with well-defined success metrics, such as code generation and mathematical reasoning.
Hugging Face Trending Papers
Jun 17
Rethinking Reward Supervision: Rubric-Conditioned Self-Distillation
Post-training of reasoning language models is commonly driven by supervised distillation and reinforcement learning with verifiable rewards. Distillation often relies on chain-of-thought annotations that are expensive to obtain and may themselves be noisy, incomplete, or partially incorrect; even when the final solution is correct, an imperfect rationale can interfere with learning.
arXiv Machine Learning
Jun 9
CATPO: Critique-Augmented Tree Policy Optimization
arXiv:2606. 08346v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a dominant paradigm for improving the reasoning capabilities of large language models (LLMs).
arXiv Machine Learning
Jun 10
Gradient-Guided Reward Optimization for Inference-time Alignment
arXiv:2606. 09635v1 Announce Type: cross Abstract: Ensuring the reliability of Large Language Models (LLMs) under distribution drift requires inference-time adaptation.