arXiv AI
CoCA: Criterion-Aware Credit Assignment for Rubric-Based Reinforcement Learning
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Oct 5
OPD Before RL: Warm-Starting Rubric-Based RL with On-Policy Distillation
The paper introduces a two‑stage training framework for rubric‑based reinforcement learning (RL) that first uses rubric‑privileged on‑policy distillation (RP‑OPD) to provide dense token‑level supervision, then applies RL to optimize the rubric reward. In RP‑OPD, a student model learns to match a rubric‑aware teacher’s next‑token distributions on student‑generated prefixes. Experiments on HealthBench, ResearchQA, and RubricHub Science show that this two‑stage approach yields the highest scores and reduces reward hacking compared to a standard SFT + RL baseline.
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.
Hugging Face Trending Papers
Aug 17
TRCA: Transition-wise Rubric Credit Assignment for Long-horizon LLM Agents
Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi-step interactions difficult. Existing approache...
arXiv AI
Aug 18
TRCA: Transition-wise Rubric Credit Assignment for Long-horizon LLM Agents
arXiv:2608. 16156v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi-step interactions difficult.
arXiv AI
Aug 11
RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning
arXiv:2608. 09123v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) for open-ended tasks is challenging because responses must satisfy multidimensional criteria without following a single correct generation trajectory.
arXiv AI
Jun 18
Rethinking Reward Supervision: Rubric-Conditioned Self-Distillation
arXiv:2606. 19327v1 Announce Type: new Abstract: Post-training of reasoning language models is commonly driven by supervised distillation and reinforcement learning with verifiable rewards.