Hugging Face Trending Papers
Diagnosing and Correcting Reward Hacking in Verifiable Instruction Following
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arXiv Machine Learning
4d ago
Diagnosing and Correcting Reward Hacking in Verifiable Instruction Following
arXiv:2610.19525v1 Announce Type: new Abstract: Verifiable instruction-following benchmarks emphasize output-constraint satisfaction but can overlook whether responses meaningfully answer the underly...
arXiv AI
Jul 7
LLM-as-a-Tutor: Policy-Aware Prompt Adaptation for Non-Verifiable RL
arXiv:2607. 04412v1 Announce Type: new Abstract: Reinforcement learning (RL) for non-verifiable instruction following increasingly relies on LLM judges with prompt-specific rubrics as reward signals.
arXiv Computation and Language
Sep 1
Small Reward Models via Backward Inference
arXiv:2602.13551v3 Announce Type: replace Abstract: Reward models (RMs) play a central role throughout the language model (LM) pipeline, particularly in non-verifiable domains. However, the dominant...
arXiv AI
Jul 2
Reasoning Up the Instruction Ladder for Controllable Language Models
arXiv:2511. 04694v5 Announce Type: replace-cross Abstract: As large language model (LLM) based systems take on high-stakes roles in real-world decision-making, they must reconcile competing instructions from multiple sources within a single prompt context.
arXiv Machine Learning
Aug 3
SVR: Self-Verifying Refinement via Joint Verdict-Confidence Reinforcement Learning for Adaptive Test-Time Compute
arXiv:2607. 28457v1 Announce Type: cross Abstract: Scaling test-time computation can improve language-model reasoning, but uniform budgets waste computation on easy inputs, while verifier-guided refinement relies on external feedback.
arXiv AI
4d ago
Verifiable Process Rewards for Agentic Reasoning
The paper introduces Verifiable Process Rewards (VPR), a framework that uses symbolic or algorithmic task-solving oracles as action-level verifiers to provide dense rewards for intermediate actions in agentic reasoning tasks. VPR is applied to Sokoban, Sudoku, and Minesweeper, and improves policy performance over outcome-reward baselines. The authors also demonstrate VPR’s effectiveness in a tool‑use benchmark, highlighting its reliance on reliable task oracles and the difficulty of extending it to less structured environments.