5The Batch (DeepLearning.AI)·
RoboReward: Vision-Language Reward Models for Robot Training via RL
Researchers at Stanford and UC Berkeley developed RoboReward, a family of 4B and 8B vision-language reward models designed to provide reward signals for robot reinforcement learning across diverse robot types and tasks. The team built a novel dataset by augmenting successful robot demonstrations with synthetically generated failure examples using GPT-5 mini and Qwen3-4B, then fine-tuned Qwen3-VL models to predict task progress scores. RoboReward 8B outperformed GPT-5, GPT-5 mini, and Gemini Robotics-ER 1.5 on the new RoboRewardBench evaluation, and in real-world robot trials substantially exceeded prior reward model baselines while still falling short of human-assigned rewards. The authors also release RoboRewardBench as a community benchmark for reward model evaluation.
Related guides (5)
Evaluation and BenchmarkingTopic guide
Evaluation and Benchmarking: The Measurement Crisis at the Frontier
Read asIn-depth
Related events (8)
Qwen2.5-VL-32B: Reinforcement-Learning-Optimized Vision-Language Model Released
Alibaba's Qwen team has released Qwen2.5-VL-32B-Instruct, a 32-billion-parameter vision-language model built on the Qwen2.5-VL series and further optimized with reinforcement learning. The model is open-sourced under the Apache 2.0 license and available on Hugging Face and ModelScope. It follows the January 2025 launch of the broader Qwen2.5-VL series, positioning the 32B scale as a balance between capability and deployment practicality.
Improving Model Safety Behavior with Rule-Based Rewards
OpenAI has developed a method called Rule-Based Rewards (RBRs) that trains models to behave safely without requiring extensive human data collection. The approach uses explicit rules to generate reward signals during training, offering a more scalable alternative to traditional RLHF-based safety alignment. This represents a practical contribution to alignment methodology from a Tier 1 lab.
LabVLA: Vision-Language-Action model and RoboGenesis data engine for scientific laboratory robotics
Researchers introduce LabVLA, a Vision-Language-Action model designed to bridge written scientific protocols and physical robot execution in laboratory settings. To address the data scarcity problem, they build RoboGenesis, a simulation-based data engine that composes lab workflows from atomic skills and generates structured demonstrations across robot embodiments. LabVLA uses a two-stage training recipe combining FAST action token pretraining on a Qwen3-VL-4B-Instruct backbone with flow matching posttraining via a DiT action expert. On the LabUtopia benchmark, LabVLA achieves the highest average success rate among evaluated baselines in both in-distribution and out-of-distribution settings.
Reinforcement Learning Recruits a Pre-Existing 'Functional Welfare' Axis in Language Models
Researchers trained language models in a semantically neutral maze environment and extracted concept vectors for rewarded and punished trajectories, finding that RL recruits a pre-existing representational axis encoding functional welfare—how well or badly the system is doing relative to its goals. The punishment vector promotes failure tokens, aligns with negative emotion concepts, and induces refusal and uncertainty when used for steering; the reward vector is its near-antiparallel mirror. Critically, these vectors are effective in models before maze training and appear in pretrain-only models, suggesting the welfare axis pre-exists post-training rather than being created by it. The findings have implications for interpretability, alignment, and understanding how minimal reward signals can broadly reshape model behavior.
Skill-RM: A unified reward model framework treating evaluation as an agentic skill
Researchers from the Qwen team propose Skill-RM, a framework that reformulates reward modeling as the execution of a reusable 'Reward-Evaluation Skill,' enabling a single model to orchestrate heterogeneous evaluation criteria including rule-based verifiers, ground-truth references, and rubrics. By treating reward computation as a structured agentic task, Skill-RM dynamically selects and aggregates evidence per input rather than relying on static evaluation. Experiments on reward benchmarks and downstream tasks (best-of-N selection, RL) show consistent improvements over traditional judge baselines. The code is publicly released under the Qwen-Applications GitHub organization.
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.
OrchRM: Self-supervised reward modeling for multi-agent orchestration without human annotations
Researchers propose Orchestration Reward Modeling (OrchRM), a self-supervised framework that trains reward models for LLM-based multi-agent orchestrators using intermediate execution artifacts to construct win-lose pairs for Bradley-Terry training. The approach avoids costly sub-agent rollouts by operating directly at the orchestration level, achieving up to 10x improvement in training token efficiency and up to 8% accuracy gains in test-time scaling. Results generalize across mathematical reasoning, web-based QA, and multi-hop reasoning tasks.
TREAD: VLM-based re-labelling framework improves robot policy generalization via dataset augmentation
TREAD (Task Robustness via Re-Labelling Vision-Action Robot Data) is a scalable framework that uses pretrained Vision-Language Models to augment existing robotics datasets without new data collection. The approach decomposes demonstrations into sub-tasks, segments videos accordingly, and generates linguistically diverse instruction labels, enriching language-action pair diversity. Evaluations on the LIBERO benchmark show improved generalization to novel tasks and goals, addressing a key limitation of current robot learning policies.