AMARIS: Memory-Augmented Rubric Improvement System for Rubric-Based Reinforcement Learning
AMARIS introduces a persistent evaluation memory system to improve rubric-based reward shaping in LLM fine-tuning via reinforcement learning. Unlike prior adaptive rubric methods that discard evaluation diagnostics after each step, AMARIS accumulates step-level summaries and retrieves relevant historical context via both static (recent steps) and dynamic (semantic similarity) retrieval to inform rubric updates. The system runs asynchronously alongside the RL training loop with approximately 5% time overhead. Experiments across closed and open-ended domains show consistent improvements over baselines, with ablations confirming that combining both retrieval modes yields the strongest results.
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PARL: Preference-Aware Rubric Learning for Personalized LLM Evaluation
This paper introduces PARL (Preference-Aware Rubric Learning), a framework that reframes personalized LLM evaluation as a learning problem rather than static judgment. PARL induces preference-aware evaluation rubrics from raw user interaction histories and uses a discriminative reinforcement learning objective to contrast user-authored responses against model outputs, capturing user-specific decision boundaries. Experiments on personalized text generation tasks show PARL produces high-fidelity rubrics that generalize across users and tasks, outperforming existing LLM-as-a-judge and automatic metric approaches.
Rubric-Conditioned Self-Distillation: structured feedback for reasoning model post-training
A new arXiv preprint proposes Rubric-Conditioned Self-Distillation (RCSD), a post-training framework that replaces scalar reward signals and noisy chain-of-thought annotations with structured rubrics for fine-grained credit assignment. The method conditions a teacher model on criterion-level rubrics to provide token-level guidance on the student's own sampled trajectories, avoiding reliance on a single reference rationale. Evaluated on science reasoning benchmarks, RCSD outperforms GRPO by 1.0 points and OPSD by 0.9 points on average.
POW3R: Policy-Aware Rubric Rewards for More Efficient RLVR Training
This paper identifies a failure mode in rubric-based reinforcement learning with verifiable rewards (RLVR): static aggregation of criterion weights conflates human-assigned importance with current optimization utility, causing many criteria to be either already saturated or unreachable. The authors introduce POW3R, a framework that dynamically reweights criterion-level rewards during training using rollout-level contrast to emphasize criteria that currently differentiate policy outputs. Across three base policies and two datasets (multimodal and text-only), POW3R wins 24 of 30 comparisons on rubric reward and strict completion metrics, and reaches equivalent performance in 2.5–4× fewer training steps than vanilla GRPO with rubric rewards.
QUBRIC: Co-designing queries and rubrics for RL beyond verifiable rewards
QUBRIC is a framework that jointly optimizes queries and rubrics for reinforcement learning in settings where rewards are not strictly verifiable. The approach uses teacher-derived key points to rewrite open-ended queries into evaluable scenarios, applies contrastive rubric generation to capture teacher-policy gaps, and filters for learnability before GRPO training. Trained only on instruction-following data, QUBRIC achieves a +5.5 point gain on ArenaHard over an SFT baseline and transfers to legal, moral, and narrative reasoning benchmarks (+6.3 points average), suggesting rubric-based RL can complement RLVR in non-verifiable domains.
DeepRubric: Evidence-tree rubric supervision cuts RL training cost for deep research agents by 13x
DeepRubric is a data construction framework that improves reinforcement learning efficiency for deep research agents by reversing the typical rubric-generation process: rather than inferring evaluation criteria from a query, it builds an evidence tree of verifiable sub-questions first, then synthesizes aligned query-rubric pairs. The authors construct 9K training examples and train DeepRubric-8B using rubric-based GRPO, achieving comparable performance to prior open-source state-of-the-art deep research models on three benchmarks while using roughly 13x fewer RL GPU-hours. The work addresses a key bottleneck in RL-based training of long-form research agents: unreliable reward signals from incomplete rubrics.
RA-RFT: Retrieval-Augmented Reinforcement Fine-Tuning teaches LLMs to reason by analogy
Researchers propose Retrieval-Augmented Reinforcement Fine-Tuning (RA-RFT), a post-training framework that trains a retriever to rank contexts by expected reasoning benefit rather than semantic similarity, then fine-tunes a policy model via reinforcement learning using retrieved analogous demonstrations. The key insight is that reasoning-relevant retrieval surfaces complementary solution strategies rather than superficially similar problems. On mathematical reasoning benchmarks, RA-RFT improves AIME 2025 average@32 accuracy by 7.1 and 2.8 points over GRPO for Qwen3-1.7B and Qwen3-4B respectively, suggesting reasoning-aware retrieval is orthogonal to reward design and training curriculum improvements.
EDIT framework trains more rubric-faithful LLM graders via internal-state diagnostics
Researchers introduce Evidence-Diagnosed Intervention Training (EDIT), a two-phase framework for improving LLM-based rubric grading. The first phase (EDIT-SFT) identifies problematic reasoning steps using posterior belief signals and input-grounding scores, then revises only those steps with rubric checklists; the second phase (EDIT-RL) uses belief-guided reward shaping to penalize harmful belief drifts during RL. Experiments on two real-world multi-subject grading benchmarks show consistent improvements over SFT and RL baselines on both in-domain and out-of-domain splits.
LongTraceRL: Reinforcement Learning for Long-Context Reasoning via Search Agent Trajectories and Rubric Rewards
LongTraceRL is a new RL training framework for improving long-context reasoning in LLMs, addressing limitations of existing RLVR methods. It constructs challenging training data using multi-hop questions from knowledge graph random walks and tiered distractors derived from search agent trajectories (high-confusability: read but uncited; low-confusability: seen but unopened). A rubric reward provides entity-level process supervision along reasoning chains, applied only to correct responses to prevent reward hacking. Experiments across three LLMs (4B–30B parameters) on five long-context benchmarks show consistent improvements over strong baselines.