arXiv AI
Information-Gain Rewards over Diversity-Pruned Tests: GT-Anchored Verifier Co-Training for Reliable Code Generation
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Reinforcement Learning without Ground-Truth Solutions can Improve LLMs
arXiv:2606. 27369v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) for training LLMs typically rely on ground-truth answers to assign rewards, limiting their applicability to tasks where the ground-truth solution is unknown.
arXiv AI
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Rubric Dropout: A Simple Way to Mitigate Reward Hacking in Rubric-as-Reward RL
arXiv:2608. 11669v1 Announce Type: cross Abstract: Reinforcement learning against rubrics, lists of criteria graded by an LLM judge, has become a standard way to post-train language models on tasks with no deterministic answer.
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Cast a Wider Net: Coordinated Pass@K Policy Optimization for Code Reasoning
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X-Coder: Advancing Competitive Programming with Synthetic Tasks, Solutions, and Tests
The paper introduces X-Coder, a competitive programming model trained entirely on synthetic tasks, verified solutions, and reliable test cases, eliminating the need for real-world data in post‑training. A dual‑verification strategy is used to reduce noise in solutions and test outputs, providing high‑quality reward signals for reinforcement learning. X‑Coder‑14B achieves significant performance gains, scoring 67.5% on LiveCodeBench v5 and 63.4% on v6, surpassing its base model by over 40 points.
arXiv AI
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What Does Multi-Harness RL Learn? Credit Assignment and Portability in Coding Agents
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arXiv AI
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CARE: Contrastive Anchor-based Rubric Evolution for Large Language Model Post-Training
arXiv:2609.00892v1 Announce Type: new Abstract: Rubric-based reinforcement learning decomposes open-ended instructions into prompt-specific, flexible rubrics, making it better suited than reinforceme...