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re- lationships. Using the Factual T5-based parser (Li et al., 2023b), these elements are extracted and then matched across candidate and reference cap- tions through a three-stage strategy involving exact, synonym, and soft matching. The final evaluation score is computed as a weighted sum of F1 scores for each elemen... | https://arxiv.org/abs/2505.22613v1 |
that: πθ(y+|x)> πθ(y−|x) DPO defines an implicit reward function based on the log-likelihood ratio between the current pol- icyπθand a fixed reference policy π0(e.g., the base model): r(y|x) = logπθ(y|x) π0(y|x) This leads to a binary classification objective that maximizes preference likelihood with KL reg-ularization... | https://arxiv.org/abs/2505.22613v1 |
TheEntropy Mechanism of Reinforcement Learning for Reasoning Language Models Ganqu Cui1∗, Yuchen Zhang1,4∗, Jiacheng Chen1∗, Lifan Yuan3, Zhi Wang5, Yuxin Zuo2, Haozhan Li2, Yuchen Fan1,Huayu Chen2,Weize Chen2,Zhiyuan Liu2,Hao Peng3,Lei Bai1,Wanli Ouyang1, Yu Cheng1,6†,Bowen Zhou1,2†,Ning Ding2,1† 1Shanghai AI Laborato... | https://arxiv.org/abs/2505.22617v1 |
Entropy and Performance . . . . . . . . . . . . . . . . . . . . . 6 2.5 Understanding the Coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3 Dynamics Analysis of Policy Entropy 8 3.1 ... | https://arxiv.org/abs/2505.22617v1 |
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 E.3 Proof for Proposition 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 E.4 Proof for Theorem 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2 1 Introduction Applied to rec... | https://arxiv.org/abs/2505.22617v1 |
a predictable manner . This empirical law results in two critical corollaries: (1) Like Scaling Laws (Kaplan et al., 2020; Hoffmann et al., 2022), the exploitation-exploration curve is predetermined given the policy model and training data. This allows us to predict policy performance at the early stage of RL and predi... | https://arxiv.org/abs/2505.22617v1 |
TAKEAWAY Without intervention, e.g., entropy or KL regularization, policy entropy is traded for reward predictably during RL. The empirical quantitative relationship between validation reward Rand policy entropy H can be expressed as R=−aexp(H+b). Then: • It suggests the predictability of policy performance from entrop... | https://arxiv.org/abs/2505.22617v1 |
3B, 7B, 32B) (Qwen et al., 2025), the Mistral family (Mistral-7B-v0.3 (Jiang et al., 2023), Mistral-Nemo-Base-2407 (MistralAI-NeMo), Mistral-Small-3.1-24B-Base-2501 (MistralAI-Small-3)), the LLaMA family (LLaMA3.2-3B (Meta-Llama- 3.2), LLaMA3.1-8B (Meta, 2024)), and DeepSeek-Math-7B-Base (Shao et al., 2024)). Tasks and... | https://arxiv.org/abs/2505.22617v1 |
precisely describe the performance-entropy relationships over all conducted experiments, with models spanning all kinds of sizes, families, and different tasks. Only 2 coefficients are needed for fitting the curve of over 200 data points, showing a high degree of regularity. The fitting results of instruct models and t... | https://arxiv.org/abs/2505.22617v1 |
breflect some intrinsic properties of the policy model and training data. 101 100 Entropy (log)20.022.525.027.530.032.535.037.5Validation Accuracy (%)GRPO RLOO PRIME REINFORCE++ Fit Figure 6: Training Qwen2.5-7B with differ- ent RL algorithms.Predicting coefficients when scaling parameters. Taking a closer look at the ... | https://arxiv.org/abs/2505.22617v1 |
claim that, if policy entropy diminishes, the ceiling not only exists, but also can be predicted. However, we argue that it is not the intrinsic limitation of RL that sets up the ceiling, but the entropy mechanism of LLMs leads to the result. Although LLMs provide us with strong policy priors, their output distribution... | https://arxiv.org/abs/2505.22617v1 |
Policy Gradient (Kakade, 2001) algorithms. Assuming that we are updating the actor policy via Policy Gradient, then zk+1 s,a−zk s,a=−η· ∇zJ(θ), where J(θ)denotes the objective function and ηdenote the learning rate. ∇zJ(θ)is calculated with Eq. 2, we have the following proposition: Proposition 1 (Difference of policy l... | https://arxiv.org/abs/2505.22617v1 |
Accuracy=0.875 Figure 8: Left: The dynamics of policy entropy (step-wise entropy difference) and covariance during on-policy GRPO training. They show similar trends as expected from the theoretical results. Right: Different prompt groups show distinct covariance behaviors. Easier prompts with higher accuracy have highe... | https://arxiv.org/abs/2505.22617v1 |
adding KL penalty between policy and reference model where LKL=L+βDKL(πθ||πref).Lis the original loss and βis the coefficient of KL loss. entropy loss. As demonstrated, entropy loss is highly sensitive to coefficients, while small coefficients have a minor influence on entropy ( 0.0001,0.001), large coefficients lead t... | https://arxiv.org/abs/2505.22617v1 |
Theorem 2 into algorithms like PPO later in this section. Supposing a batch of Nrollout tokens, πθ(yi)denotes the output probability of the policy model for token yi given its corresponding prefix. According to Theorem 2, we firstly define token-wise centered cross-product between log probability and advantage as: Cov(... | https://arxiv.org/abs/2505.22617v1 |
. mean () return pg_loss Listing 1: The pseudo-code of the policy loss computation with Clip-Cov andKL-Cov . The implementation only need to modify several lines of code. 4.3 Experiments Settings. We train Qwen2.5 models on math tasks to validate Clip-Cov andKL-Cov . We use the DAPO- MATH dataset (Yu et al., 2025) for ... | https://arxiv.org/abs/2505.22617v1 |
Training Qwen2.5-7B ( Top) / Qwen2.5-32B ( bottom ) with GRPO with/without our methods. Left: Entropy dynamics. Our methods uplift policy entropy from collapse, enabling sustained exploration. Middle: Our method also incentivizes longer responses compared with vanilla GRPO. Right: The policy model consistently outperfo... | https://arxiv.org/abs/2505.22617v1 |
Entropy-regularized reinforcement learning, also referred as maximum entropy RL (Ziebart et al., 2008; Toussaint, 2009), adopts a regularization term in reward to encourage high-entropy actions. This regularization term was widely-inherited in RL algorithms (Mnih et al., 2015, 2016; Schulman et al., 2017a,b; Haarnoja e... | https://arxiv.org/abs/2505.22617v1 |
intelligence. 15 REFERENCES References Alekh Agarwal, Sham M Kakade, Jason D Lee, and Gaurav Mahajan. On the theory of policy gradient methods: Optimality, approximation, and distribution shift. Journal ofMachine Learning Research , 22(98): 1–76, 2021. Shivam Agarwal, Zimin Zhang, Lifan Yuan, Jiawei Han, and Hao Peng. ... | https://arxiv.org/abs/2505.22617v1 |
Ou, Yuduan Wang, Yue Gong, Yuheng Zou, Yujia He, Yunfan Xiong, Yuxiang Luo, Yuxiang You, Yuxuan Liu, Yuyang Zhou, Y . X. Zhu, Yanhong Xu, Yanping Huang, Yaohui Li, Yi Zheng, Yuchen Zhu, Yunxian Ma, Ying Tang, Yukun Zha, Yuting Yan, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhean Xu, Zhenda Xie, Zhengyan Zhang, Zhewen ... | https://arxiv.org/abs/2505.22617v1 |
model. arXiv preprint arXiv:2503.24290, 2025. Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thom... | https://arxiv.org/abs/2505.22617v1 |
Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances inneural information processing systems, 35:27730–27744, 2022. Qwen, An Yang, Baosong Yang, Beichen Zhang... | https://arxiv.org/abs/2505.22617v1 |
Xiaochen Zuo, Yu Yue, Tiantian Fan, Gaohong Liu, Lingjun Liu, Xin Liu, Haibin Lin, Zhiqi Lin, Bole Ma, Guangming Sheng, Yuxuan Tong, Chi Zhang, Mofan Zhang, Wang Zhang, Hang Zhu, Jinhua Zhu, Jiaze Chen, Jiangjie Chen, Chengyi Wang, Honglin Yu, Weinan Dai, Yuxuan Song, Xiang Wei, Haodong Zhou, Jingjing Liu, Wei Ma, Ya-Q... | https://arxiv.org/abs/2505.22617v1 |
function given state s) E a∼πθ(·|s)[Aπθ(s, a)] = E a∼πθ(·|s)[Qπθ(s, a)−Vπθ(s)] =E a∼πθ(·|s)[Q(s, a)]−E a∼πθ(·|s)[V(s)] =V(s)−V(s) = 0 E.2 Proof for Lemma 1 Lemma 1 :Let the actor policy πθbe a tabular softmax policy, the difference of information entropy given statesbetween two consecutive steps satisfies H(πk+1 θ|s)− ... | https://arxiv.org/abs/2505.22617v1 |
arXiv:2505.22626v1 [cs.RO] 28 May 2025SCIZOR : A Self-Supervised Approach to Data Curation for Large-Scale Imitation Learning Yu Zhang1∗, Yuqi Xie1,2∗, Huihan Liu1†, Rutav Shah1†, Michael Wan‡2, Linxi “Jim” Fan2, Yuke Zhu1,2 1The University of Texas at Austin2NVIDIA Research Abstract: Imitation learning advances robot ... | https://arxiv.org/abs/2505.22626v1 |
removal module and the redundant transitions removal module. Each module removes data based on its own threshold, resulting in a curated dataset. A policy trained on the curated dataset achieves a higher success rate. to maximize data utilization is the need for curating at the finest granularity: we must evaluate the ... | https://arxiv.org/abs/2505.22626v1 |
performance across diverse large-scale imitation learning benchmarks, showing on average 15.4% improvement. 2 Related Work Imitation Learning on Large-Scale Robot Datasets. Imitation learning has been a popular ap- proach to learn robot policy from human demonstrations [18, 19, 1, 20] to scale up robot policy generaliz... | https://arxiv.org/abs/2505.22626v1 |
Decision Process M= (S,A,R,P, p0, γ) representing the state space, action space, reward function, transition probability, initial state dis- tribution, and discount factor. Given the current state st∈ S , the robot action at∈ A is drawn from the policy π(· |st). The objective of imitation learning is to learn a policy ... | https://arxiv.org/abs/2505.22626v1 |
into Bbins, where each bin is a time interval in seconds. To predict task progress for a sub-trajectory Si,i+T, we train a task progress classifier to classify the bin cor- 4 responding to the time Tbetween the start and end states. Empirically, we set B= 5, sample each sub-trajectory as 2seconds, and bins to be [0,0.5... | https://arxiv.org/abs/2505.22626v1 |
actions to identify and filter redundant state-action pairs. Defining State-Action Duplicates. Prior work on semantic deduplication [7] has focused on curat- ing large image datasets by removing semantically similar data pairs based solely on visual features. However, such visual-only deduplication methods are not well... | https://arxiv.org/abs/2505.22626v1 |
see Appendix B.1. •Open-X-Embodiment (OXE) [29] : A large-scale collection of over one million real-world robotic trajectories. We use the Simpler environment [46] and benchmark on two tasks: Pick Can andMove Near . We train the Octo model [13] with two random seeds and use the same “Magic Soup” weighting. This setting... | https://arxiv.org/abs/2505.22626v1 |
environments. RQ2: What advantage does S CIZOR ’s fine-grained state-action curation offer over trajectory- or dataset-level curation in prior work? To validate the effectiveness of fine-grained curation on state-action pairs, we compare S CIZOR with two baseline methods: a trajectory-level curation method, Deminf [6],... | https://arxiv.org/abs/2505.22626v1 |
seeds for faster iteration. Omitting either component consistently degrades performance across all four tasks, high- lighting their importance. Temporal discounting lets S CIZOR propagate evidence of suboptimality backward in time, so that transitions leading to poorer future states can also be identified in addition t... | https://arxiv.org/abs/2505.22626v1 |
49] that better integrate the action and state spaces. Dependence on Demonstration Quality: SCIZOR assumes that most demonstrations within a tra- jectory are of good quality, as we rely on self-supervised learning to learn from the majority of the data. If poor-quality demonstrations dominate, the method may become les... | https://arxiv.org/abs/2505.22626v1 |
O. M. Team, D. Ghosh, H. Walke, K. Pertsch, K. Black, O. Mees, S. Dasari, J. Hejna, T. Kreiman, C. Xu, J. Luo, Y . L. Tan, L. Y . Chen, P. Sanketi, Q. Vuong, T. Xiao, D. Sadigh, C. Finn, and S. Levine. Octo: An open-source generalist robot policy, 2024. URL https: //arxiv.org/abs/2405.12213 . [14] A. S. Chen, A. M. Les... | https://arxiv.org/abs/2505.22626v1 |
N. Cherniadev, X. Da, R. Ding, L. J. Fan, Y . Fang, D. Fox, F. Hu, S. Huang, J. Jang, Z. Jiang, J. Kautz, K. Kundalia, L. Lao, Z. Li, Z. Lin, K. Lin, G. Liu, E. Llontop, L. Magne, A. Mandlekar, A. Narayan, S. Nasiriany, S. Reed, Y . L. Tan, G. Wang, Z. Wang, J. Wang, Q. Wang, J. Xiang, Y . Xie, Y . Xu, Z. Xu, S. Ye, Z.... | https://arxiv.org/abs/2505.22626v1 |
W. Gramlich, T. Hage, A. Herzog, J. Hoech, T. Nguyen, I. Storz, B. Tabanpour, L. Takayama, J. Tompson, A. Wahid, T. Wahrburg, S. Xu, S. Yaroshenko, K. Zakka, and T. Z. Zhao. Aloha 2: An enhanced low-cost hardware for bimanual teleoperation, 2024. [36] A. Iyer, Z. Peng, Y . Dai, I. Guzey, S. Haldar, S. Chintala, and L. ... | https://arxiv.org/abs/2505.22626v1 |
. Betala, E. Zhang, J. Liu, C. Ding, and Y . Zhu. Multi-task interactive robot fleet learning with visual world models, 2024. URL https://arxiv.org/abs/2410.22689 . [48] K. Pertsch, K. Stachowicz, B. Ichter, D. Driess, S. Nair, Q. Vuong, O. Mees, C. Finn, and S. Levine. Fast: Efficient action tokenization for vision-la... | https://arxiv.org/abs/2505.22626v1 |
two feature vectors to obtain a delta feature vector, which emphasizes task-relevant changes and accelerates convergence while discarding redundant static information and is concatenated with a CLS token. The feature vector is then processed through a series of multi-layer self-attention transformer blocks. The output ... | https://arxiv.org/abs/2505.22626v1 |
learning algorithms. We intentionally leave all model architectures and hyper- parameters unchanged from the public reference implementations of each dataset, demonstrating that S CIZOR isplug-and-play . For the RoboMimic andSirius-Fleet experiments, we train each model using three random seeds. For the larger OXE expe... | https://arxiv.org/abs/2505.22626v1 |
page 16 continued from previous page Visualization Task Name and Description Mutex Bread to Plate The robot is instructed to pick up the bread and then place it on the white plate. Mutex Mug to Plate The robot is instructed to pick up the pink mug and then place it on the white plate. Mutex Bowl to Plate The robot is i... | https://arxiv.org/abs/2505.22626v1 |
gripper moved towards the bowl at a slow pace. Pause : The robot arm stopped at behind the cereal box for a long time. Figure 7: Visualizations for suboptimal scenarios detected in Robomimic dataset 19 Move Back and Forth : The robot arm moved aimlessly and didn’t contribute to the task progress. Manipulation Failure :... | https://arxiv.org/abs/2505.22626v1 |
Spatial Knowledge Graph-Guided Multimodal Synthesis Yida Xue1, Zhen Bi1,2, Jinnan Yang2, Jungang Lou2, Huajun Chen1,Ningyu Zhang1∗ 1Zhejiang University2Huzhou University {xueyida, bizhen_zju, huajunsir, zhangningyu}@zju.edu.cn Abstract Recent advances in multimodal large language models(MLLMs)havesignificantlyenhanced ... | https://arxiv.org/abs/2505.22633v1 |
sentspatialrelationshipsthroughtriplets. Formally, SKG can be represented as: 𝐺=(𝐸,𝑇) (1) where𝐸denotesthesetofnodes(objects)and 𝑇denotes the set of triplets describing spatial rela- tions. These relationships may include directional relationships (e.g., “to the left of” and “to the right of”),distancerelationship... | https://arxiv.org/abs/2505.22633v1 |
capabilitiestosynthesizeentitiesandtheirspatial relationship triplets. Intheinitialphase, 𝑓1isusedtoselectasubset {𝑜𝑖 𝑗1,𝑜𝑖 𝑗2,...,𝑜 𝑖 𝑗𝑘}of𝑘candidate objects from theoriginalset{𝑜𝑖1,𝑜𝑖2,...,𝑜 𝑖𝑛}. Theseselected objectsareutilizedtoconstructaspecificSKG.The subset𝐶𝑘is formally represented as: 𝐶𝑘=... | https://arxiv.org/abs/2505.22633v1 |
only improves the accuracy of the data generation process but also reducestheoccurrenceofhallucinations. Further- more, it enables the creation of more diverse and complexquestionstailoredforspatialunderstand- ing tasks. These questions are categorized into two types based on their primary focus: Entity-Based DataandRe... | https://arxiv.org/abs/2505.22633v1 |
with a score of 79.3 (+3.9) ↑, and Fine-tuned Llama-3.2-vision impressively achieves 59.8(+13.9) ↑,suggestingsignificantspatialunder- standing advancements. In the MMVP bench- mark, Fine-tuned LLaVA-1.6 shows notable im- provement with a score of 36.7 (+4.7) ↑, while Fine- tuned Llama-3.2-vision also makes gains, reach... | https://arxiv.org/abs/2505.22633v1 |
improves the performance of MLLMsin visual detail tasks Our experiments demonstrate that synthesizingdatawithvaryingquantitiesofobjects improves spatialunderstanding, with greater gains observed as the number of objects increases in MMVP benchmark. Notably, training with more objectssignificantlyenhancesfine-grainedrec... | https://arxiv.org/abs/2505.22633v1 |
Limitations Noiseinsyntheticdata. Despitetheimplemen- tation of automatic filtering, the synthesized data may still contain noise. This noise arises from potential mismatches between images and text in diffusionmodels, aswellastheinherentpossibility of hallucinations in LLMs. Difficult to synthesize data with a large n... | https://arxiv.org/abs/2505.22633v1 |
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. CoRR, abs/2106.09685. Amita Kamath, Jack Hessel, and Kai-Wei Chang. 2023. What’s" up" with vision-language models? investi- gating their struggle with spatial r... | https://arxiv.org/abs/2505.22633v1 |
mod- els.CoRR, abs/2112.10752. FatemehShiri,Xiao-YuGuo,MonaFar,XinYu,Reza Haf, and Yuan-Fang Li. 2024. An empirical analysis on spatial reasoning capabilities of large multimodal models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miami, FL, USA, November 12-16... | https://arxiv.org/abs/2505.22633v1 |
Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023b. A survey of large language models. arXiv preprint arXiv:2303.18223 . Kening Zheng, Junkai Chen, Yibo Yan, Xin Zou, and Xuming Hu. 2024. Reefknot: A comprehensive benchmarkforrelationhallucinationevaluation,anal- ysis and mitigation in multimodal large... | https://arxiv.org/abs/2505.22633v1 |
task is to first select a subset of candidate objects, and then describe the attributes of the selected objects. Your response should meet the following requirements: 1. Select 2 to 4 suitable objects from the given objects to form a possible combination. Note that for some candidate objects that are suitable for repea... | https://arxiv.org/abs/2505.22633v1 |
not overlap or go beyond the image boundaries. Each bounding box should be in the format of (object name, [top-left x coordinate, top-left y coordinate, box width, box height]) and should not include more than one object. Do not put objects that are already provided in the bounding boxes into the background prompt. Do ... | https://arxiv.org/abs/2505.22633v1 |
question-answer pairs. ... 4. Use the template in the format of a JSON list to generate question and answer pairs without any other information. Below are reference responses for saving and not saving this image: <In context learning> <image> Prompt for "Textual Data Filtering": You are an AI assistant specializing in ... | https://arxiv.org/abs/2505.22633v1 |
arXiv:2505.22635v1 [cs.CL] 28 May 2025Learning Composable Chains-of-Thought Fangcong Yin♠, Zeyu Leo Liu♠, Liu Leqi♠, Xi Ye♢, Greg Durrett♠ ♠The University of Texas at Austin,♢Princeton University, fangcongyin@utexas.edu Abstract A common approach for teaching large language models (LLMs) to reason is to train on chain-... | https://arxiv.org/abs/2505.22635v1 |
sequence and multiply the ASCII value by 5: Good nightEvaluation: Compositional SkillQ: Find the next letter in the alphabet following the last letter in the sequence: What a nice day Composable CoT: <prefix> The last letter is y, and the letter following it in the alphabet is z. So the answer is z. </prefix>Composable C... | https://arxiv.org/abs/2505.22635v1 |
qthat states a reasoning problem, an LLM Mis prompted to solve it by drawing samples from conditional distribution ˜y∼pM(y|q). Letadenote the ground truth answer to q. We consider two common ways of solving q: (1) Direct answer :˜yonly contains the predicted answer ˜a; (2) Reasoning with chain-of-thought :˜yincludes a ... | https://arxiv.org/abs/2505.22635v1 |
dataset DCoT T={(q,t, a)}forT ∈ {T i,Tj} and we call it standard CoT data. Given a pair of atomic tasks, we as- sume the CoT traces in each atomic task data follow a certain distribution distinct to that dataset. A pre-trained LLM M0fine-tuned on the standard CoT data is only optimized to gener- ate CoT traces that can... | https://arxiv.org/abs/2505.22635v1 |
combine multiple models into a single multi-task model [ 12,13,14]. Starting from M0, we fine-tune two models MiandMj(parametrized byθiandθj) onDaug TiandDaug Tjrespectively to optimize for atomic task performance. Then we use Task Arithmetic [ 12] to merge the two models into a single model Mcomb parametrized by θcomb... | https://arxiv.org/abs/2505.22635v1 |
the following atomic tasks that involve string operations. (1) Last letter in alphabet : Determine the next letter in the alphabet following the last letter in a sequence of letters. (2) Letter concentation : Adapted from [ 9,20,4], this task prompts the LLM to concatenate the first, second, second-to-last, or last let... | https://arxiv.org/abs/2505.22635v1 |
zero-shot setting does not use further tuning. Few-shot direct answer prompting: we prompt M0with 5-shot demonstrations drawn from the compositional data; (2) Few-shot CoT prompting: we prompt M0with 5-shot CoT demonstrations drawn from the atomic data; (3) Model merging of atomic CoT models ( StandardCoT-Merge ): we f... | https://arxiv.org/abs/2505.22635v1 |
model merging or multitask learning to combine atomic CoT models trained on Composable CoT data outperforms baselines across settings and models, and is sometimes comparable to SFT with compositional supervision. MethodsLast Letter Concat Concat Skill-Mix Literary + Mult + Last Letter + Mult + Rhetorical EM EM EM Full ... | https://arxiv.org/abs/2505.22635v1 |
stands for supervised fine-tuning with the compositional answer data; CFT stands for continued fine-tuning; MTL stands for multitask learning method. Results on last letter + mult are omitted because the zero-shot performance already saturates. RFT on ComposableCoT variants achieves the best compositional task performa... | https://arxiv.org/abs/2505.22635v1 |
of generated responses that use the CoT format of the first atomic task of the composition, and likewise for the second.†denotes that the ComposableCoT method has a significantly higher “% Both CoT” than the StandardCoT counterpart at the 0.01level using a paired bootstrap test. Combined Composable CoT models generate ... | https://arxiv.org/abs/2505.22635v1 |
composable CoT shows a promising approach to improve compositional reasoning in LLMs, and could be extended to build more efficient and robust large reasoning models. Limitations: Our experiments focus on pairwise compositional tasks where two atomic reasoning skills are composed and do not cover compositions of more t... | https://arxiv.org/abs/2505.22635v1 |
Hui Qu, Hui Li, Jianzhong Guo, Jiashi Li, Jiawei Wang, Jingchang Chen, Jingyang Yuan, Junjie Qiu, Junlong Li, J. L. Cai, Jiaqi Ni, Jian Liang, Jin Chen, Kai Dong, Kai Hu, Kaige Gao, Kang Guan, Kexin Huang, Kuai Yu, Lean Wang, Lecong Zhang, Liang Zhao, Litong Wang, Liyue Zhang, Lei Xu, Leyi Xia, Mingchuan Zhang, Minghua... | https://arxiv.org/abs/2505.22635v1 |
H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems , 2022. [11] Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto. s1: Simple test-time s... | https://arxiv.org/abs/2505.22635v1 |
Channing Conger, Charlotte Barette, Chelsea V oss, Chen Ding, Cheng Lu, Chong Zhang, Chris Beaumont, Chris Hallacy, Chris Koch, Christian Gibson, Christina Kim, Christine Choi, Christine McLeavey, Christopher Hesse, Claudia Fischer, Clemens Winter, Coley Czarnecki, Colin Jarvis, Colin Wei, Constantin Koumouzelis, Dane ... | https://arxiv.org/abs/2505.22635v1 |
Rachel Lim, Rahul Arora, Rajan Troll, Randall Lin, Rapha Gontijo Lopes, Raul Puri, Reah Miyara, Reimar Leike, Renaud Gaubert, Reza Zamani, Ricky Wang, Rob Donnelly, Rob Honsby, Rocky Smith, Rohan Sahai, Rohit Ramchandani, Romain Huet, Rory Carmichael, Rowan Zellers, Roy Chen, Ruby Chen, Ruslan Nigmatullin, Ryan Cheu, S... | https://arxiv.org/abs/2505.22635v1 |
Yuntian Deng, Yejin Choi, and Stuart Shieber. From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step . arXiv preprint arXiv:2405.14838 , 2024. [27] Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, and Yuandong Tian. Training large language models to reason in a continuo... | https://arxiv.org/abs/2505.22635v1 |
Srinivasan Iyer, Asli Celikyilmaz, Veselin Stoyanov, Greg Durrett, and Ramakanth Pasunuru. Complementary explanations for effective in-context learning. In Findings of the Association for Computational Linguistics: ACL 2023 , Toronto, Canada, July 2023. Association for Computational Linguistics. [42] Zayne Rea Sprague,... | https://arxiv.org/abs/2505.22635v1 |
CoT-Valve: Length-Compressible Chain-of-Thought Tuning. arXiv preprint arXiv:2502.09601 , 2025. [51] Derek Tam, Yash Kant, Brian Lester, Igor Gilitschenski, and Colin Raffel. Realistic evaluation of model merging for compositional generalization. arXiv preprint arXiv:2409.18314 , 2024. [52] Fangcong Yin, Xi Ye, and Gre... | https://arxiv.org/abs/2505.22635v1 |
fine-tuning models on Composable CoT datasets that only have suffix CoT examples . Note that this is different from the multitask learning setting discussed in Section 3.1 where the Composable CoT dataset consists of both the prefix CoT examples and the suffix CoT examples. This experiment mainly aims at stress testing... | https://arxiv.org/abs/2505.22635v1 |
decision to exclude one variant of concatenation + last letter that concatenates the last letter of each word and finds the next letter following the last letter in the concatenated sequence; this variant can be solved by the reasoning shortcut of only applying Last letter in alphabet rather than a composition of both.... | https://arxiv.org/abs/2505.22635v1 |
other categories have more (e.g. the logical and rhetorical skills have a large body of overlaps). 2.Removing the requirements of post-hoc explanation and refinement from the prompt. The original dataset evaluates models by prompting the models to first write a sentence, provide an explanation for the written sentence,... | https://arxiv.org/abs/2505.22635v1 |
natural piece of text with up to a few lines in the context of Hiking that illustrates all of the following skills: begging the question or assuming the conclusion. Please keep the text as short as 19 possible, and make sure the concepts can be found fully from the text. For reference, here are the definitions and exam... | https://arxiv.org/abs/2505.22635v1 |
77.6 76.7 81.9 D.2 Evaluation Metrics We use GPT-4o-mini as the LLM-as-a-judge to grade the generated sentence using the exact same grading rubric as provided by [ 19]; the grader judges the quality of the sentence based on if: (1) All skills are used; (2) The sentence makes sense; (3) The sentence attaches to the give... | https://arxiv.org/abs/2505.22635v1 |
1e-4 1e-4 1e-3 1e-3 Qwen 2.5-7B SFTSFT on Base Model - 1e-3 1e-3 5e-4 CFT on StandardCoT-Merge - 5e-4 5e-4 1e-4 CFT on StandardCoT-MTL - 1e-3 1e-3 1e-3 MTL StandardCoT + Comp Answer - 5e-4 5e-4 1e-3 RFTStandardCoT-MTL - 1e-3 1e-4 5e-4 ComposableCoT-MTL (Ours) - 1e-3 1e-3 5e-4 F.2 Configuration for Rejection Sampling Fi... | https://arxiv.org/abs/2505.22635v1 |
number of training data used by each zero-shot method for both models and in Table 9 for Qwen 2.5-7B by each method with compositional answer data in the experiments in Section 5.2. Note that the statistics for Llama 2-7B in the setting with compositional supervision are mostly the same except N(i,j)= 500 for concat + ... | https://arxiv.org/abs/2505.22635v1 |
- Evaluation with Limited Compositional Answer Data CFT on StandardCoT-Merge 2 Standard Merge StandardCoT-Merge CFT CFT on StandardCoT-MTL 1 Standard MTL StandardCoT-MTL CFT MTL on StandardCoT + Comp Answer - Standard - Pretrained ModelMix with Atomic CoT data and MTL RFT on StandardCoT-Merge 2 Standard Merge StandardC... | https://arxiv.org/abs/2505.22635v1 |
FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control Younggyo Seo1Carmelo Sferrazza1Haoran Geng1 Michal Nauman1,2Zhao-Heng Yin1Pieter Abbeel1 1University of California, Berkeley2University of Warsaw https://younggyo.me/fast_td3 Abstract Reinforcement learning (RL) has driven significant progre... | https://arxiv.org/abs/2505.22642v1 |
fast with massively parallel simulation (Heess et al., 2017; Hwangbo et al., 2019). However, PPO is an on-policy algorithm and not sample-efficient, making it difficult to fine-tune it during real-world deployment or initialize training with demonstrations (Hester et al., 2018). Meanwhile, recent off-policy RL research... | https://arxiv.org/abs/2505.22642v1 |
Playground, and the codebase also supports several user-friendly features, such as preconfigured hyperparameters, rendering support, logging, and loading checkpoints. We note that our work is orthogonal to latest RL research, such as SR-SAC (D’Oro et al., 2023), BBF (Schwarzer et al., 2023), BRO (Nauman et al., 2024b),... | https://arxiv.org/abs/2505.22642v1 |
parallel simulation is particularly effective, because the randomness from parallel environments increases diversity in the data distri- bution. This enables TD3 to leverage its strength – efficient exploitation of value functions – while mitigating its weakness in exploration. Large-batch training We find that using a... | https://arxiv.org/abs/2505.22642v1 |
large σmax= 0.4is helpful for FastTD3 as shown in Li et al. (2023b). Update-to-data ratio In contrast to prior work showing that increasing the update-to-data (UTD) ratio – that is, the number of gradient updates per environment step – typically requires additional techniques (D’Oro et al., 2023; Schwarzer et al., 2023... | https://arxiv.org/abs/2505.22642v1 |
D512 (0.55M) D1024 (1.83M)(a) Effect of model size 0 2400 4800 7200 9600 Wall Time (Seconds)02004006008001000h1hand_hurdle 0 2400 4800 7200 9600 Wall Time (Seconds)02004006008001000h1hand_slide Max 0.001 Max 0.1 Max 0.4 (b) Effect of noise scales 0 3000 6000 9000 12000 15000 Wall Time (Seconds)02004006008001000h1hand_s... | https://arxiv.org/abs/2505.22642v1 |
4SimbaV2 uses action repeat of 2 for HumanoidBench in their paper, which is unusual for joint position control. We thus run our SimbaV2 experiments with action repeat of 1. 6 (a) Gait of FastTD3 trained with PPO-tuned reward (b) Gait of PPO trained with PPO-tuned reward (c) Gait of FastTD3 trained with FastTD3-tuned re... | https://arxiv.org/abs/2505.22642v1 |
Wall Time (Seconds)500 250 02505007501000h1hand_push SAC FastTD3 FastSACFigure 8: FastSAC. We develop FastSAC, a variant of SAC that incorporates our FastTD3 recipe. We find that FastSAC is significantly faster than vanilla SAC, though still slower than FastTD3. Sim-to-real RL with FastTD3 For the sim-to-real experimen... | https://arxiv.org/abs/2505.22642v1 |
MURI N00014-22-1-2773. Pieter Abbeel holds concurrent appointments as a Professor at UC Berkeley and as an Amazon Scholar. This paper describes work performed at UC Berkeley and is not associated with Amazon. We thank NVIDIA for providing compute resources through the NVIDIA Academic DGX Grant. References Ba, Jimmy Lei... | https://arxiv.org/abs/2505.22642v1 |
deep reinforcement learning. In International Conference on Machine Learning , 2025. Li, Tianhong, Chang, Huiwen, Mishra, Shlok Kumar, Zhang, Han, Katabi, Dina, and Krishnan, Dilip. Mage: Masked generative encoder to unify representation learning and image synthesis. In Proceedings of the IEEE/CVF Conference on Compute... | https://arxiv.org/abs/2505.22642v1 |
better, faster: Human-level atari with human-level efficiency. In International Conference on Machine Learning , 2023. Seo, Younggyo and Abbeel, Pieter. Reinforcement learning with action sequence for data-efficient robot learning. arXiv preprint arXiv:2411.12155 , 2024. Sferrazza, Carmelo, Huang, Dun-Ming, Lin, Xingyu... | https://arxiv.org/abs/2505.22642v1 |
(Seconds)10 0102030Isaac-Velocity-Rough-H1-v0 0.0 0.2 0.4 0.6 0.8 1.00.00.20.40.60.81.0 0.0 0.2 0.4 0.6 0.8 1.00.00.20.40.60.81.0 PPO FastTD3 Figure 10: IsaacLab results. We provide learning curves on six tasks from IsaacLab (Mittal et al., 2023). The solid line and shaded regions represent the mean and standard deviat... | https://arxiv.org/abs/2505.22642v1 |
Pre-training for Recommendation Unlearning Guoxuan Chen The University of Hong Kong Hong Kong guoxchen@foxmail.comLianghao Xia The University of Hong Kong Hong Kong aka_xia@foxmail.comChao Huang∗ The University of Hong Kong Hong Kong chaohuang75@gmail.com Abstract Modern recommender systems powered by Graph Neural Netw... | https://arxiv.org/abs/2505.22649v1 |
systems. Specifically, there is a growing need for recommendation unlearning, which involves removing specific user interactions and preferences from trained recommender models in compliance with user requests or regulatory requirements. Graph Neural Networks (GNNs) have advanced Collaborative Filtering, establishing t... | https://arxiv.org/abs/2505.22649v1 |
an item setV(|V|=𝐽), and a user-item interaction matrix A∈R𝐼×𝐽 are utilized to represent the historical interaction records, based on which the encoders derive embeddings and make predictions. For each entry𝑎𝑖,𝑗in the interaction matrix A,𝑎𝑖,𝑗=1if user𝑢𝑖∈U has interacted with item 𝑣𝑗∈V, otherwise𝑎𝑖,𝑗=0.... | https://arxiv.org/abs/2505.22649v1 |
useEΔto denote the set of edges to be undone and E𝑟to denote remaining edges,i.e., EΔ=E\E𝑟. In practice, pairs in Enot only form the adjacency matrix for GNN’s forward propagation but also serve as the positive supervisory signals in the loss function( Eq. 4). Therefore, dropping a subset of edges not only changes th... | https://arxiv.org/abs/2505.22649v1 |
embeddings before unlearning, and ˜𝑐1,˜𝑐2>0. Since the original negative pair (𝑢𝑖,𝑣𝑗−) has already been pushed away from each other, it is unneces- sary to do that again. The only valuable part for unlearning is¯e(𝑢) 𝑖≈¯e(𝑡) 𝑖−˜𝑐1¯e(𝑡) 𝑗+because only(𝑢𝑖,𝑣𝑗+)is changed from positive to negative. Extendi... | https://arxiv.org/abs/2505.22649v1 |
adjacent matrix built from E𝑟. Pa- rameters in IE(·)should be well-pretrained in advance. 3.3 Multi-task Loss Functions for Pretraining We now introduce the loss functions for UnlearnRec’s pretraining paradigm, in which the only trainable parameters are H0andW𝜂 during pretraining. MLP(·)will be updated during fine-tu... | https://arxiv.org/abs/2505.22649v1 |
unlearning requests during the finetuning. 3.4.1 Pre-training. In each simulation round, we randomly select a subsetE(𝑠) ΔfromE(e.g.,𝜌%) as the simulated unlearning set to construct the IDM ¯A(𝑠) Δ. Then, we optimize the loss function L to train the parameters. Currently, only H0in Eq. 15 and W𝜂in Eq. 16 are traina... | https://arxiv.org/abs/2505.22649v1 |
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