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arXiv:2505.18126v1 [cs.LG] 23 May 2025Reward Model Overoptimisation in Iterated RLHF Lorenz Wolf∗ UCL Centre for Artificial Intelligence Department of Computer Science University College LondonRobert Kirk UK AI Security Institute Mirco Musolesi UCL Centre for Artificial Intelligence Department of Computer Science Unive... | https://arxiv.org/abs/2505.18126v1 |
RLHF. We identify three pivotal design choices, highlighted in Figure 1, that critically influence the success or failure of the process: preference data management (i.e., whether to aggregate or isolate preference data across iterations), reward function formulation (i.e., the choice of reward signal to optimize in su... | https://arxiv.org/abs/2505.18126v1 |
also performative RL [22]. Overoptimisation is a common issue in RL, and evidence of this has been frequently reported in the RLHF literature [ 46,37,11,35]. A promising method to mitigate overoptimisation is using reward model ensembles combined with conservative optimisation as proposed by [ 5]. Several works further... | https://arxiv.org/abs/2505.18126v1 |
yields the overall reward maximised as RPPO(x, y) =Rϕ(x, y)−βlogπθ(y|x) πsft(y|x) , (2) 3 Algorithm 1 Iterated RLHF (design choices highlighted) 1:Inputs: Prompt dataset X={xi}N i=1, πsft,Rinit,R⋆, # of iterations niter 2:π0←πsft 3:fork= 1toniterdo 4: yi,0, yi,1∼πk−1(xi)∀xi∈X 5: pi←R⋆(xi, yi,0, yi,1)∀xi∈D 6: ˜Dk← {xi... | https://arxiv.org/abs/2505.18126v1 |
data. Given a list of kpreference datasets, the responses in each of which have been generated by different policies π1, ..., π k−1, we identify three possible options to consolidate them into a single training dataset. The first option (Figure 3.a) is to simply set Dk=˜Dk, only training the reward model on the prefere... | https://arxiv.org/abs/2505.18126v1 |
fine-tuned policies π1, ..., π k−1, how can we choose πinit kto balance efficiency and robustness against overoptimisation? Policy initialisation. The final design choice concerns the initialisation of the policy, i.e., how πinit k is chosen. The authors of [ 2] initialise the policy from πsftat every iteration, not ta... | https://arxiv.org/abs/2505.18126v1 |
C. We perform a total of 4 iterations per method and report the results of the final iteration in comparison to the initial one. All results presented in our performance evaluation are reported for 8 random seeds, except for policy initialisation From SFT with the Take last configuration for both preference data and re... | https://arxiv.org/abs/2505.18126v1 |
300 400500 KL (nats)0.00.20.40.60.81.0Gold Score Gold points Proxy pointsGold score Proxy scoreIteration 1 Iteration 2Iteration 3 Iteration 4100 200 300 KL (nats)0.000.020.040.060.080.100.120.14MMD 1.0 0.5 0.00.51.01.52.0 Proxy ScoreFigure 4: Progression of proxy and gold reward alignment across RLHF iterations with po... | https://arxiv.org/abs/2505.18126v1 |
a slight advantage in the mid-KL regime (100-250 nats), the differences are modest, suggesting that prompt repetition plays a minor role in model performance and mitigating overoptimisation. These observations also hold throughout the earlier iterations (see Appendix E.2), highlighting that while data combination strat... | https://arxiv.org/abs/2505.18126v1 |
In contrast to [ 5,33], we ensemble and weight average models that have been trained on data with significantly different joint distribution over pairs (x, y). Regardless, both methods still provide significant improvements when comparing the fourth and first iterations. The various reward model combi- nation methods i... | https://arxiv.org/abs/2505.18126v1 |
hard to recover from. While From SFT is reset at the end of each iteration, LITI andTake last have to recover form the initial overoptimisation, as shown in Figure 7b. The behaviour in earlier iterations reveals the consistent performance improvements attained with LITI. On the other hand, Take last is unable to recove... | https://arxiv.org/abs/2505.18126v1 |
block designs: I. the method of paired comparisons. Biometrika , 39(3/4):324–345, 1952. [4]Lichang Chen, Chen Zhu, Jiuhai Chen, Davit Soselia, Tianyi Zhou, Tom Goldstein, Heng Huang, Mohammad Shoeybi, and Bryan Catanzaro. ODIN: Disentangled reward mitigates hacking in RLHF. In Proceedings of the 41st International Conf... | https://arxiv.org/abs/2505.18126v1 |
Pyatkin, Jacob Morrison, LJ Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, Noah A. Smith, and Hannaneh Hajishirzi. RewardBench: Evaluating Reward Models for Language Modeling. arXiv preprint arXiv:2403.13787 , 2024. [19] Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen ... | https://arxiv.org/abs/2505.18126v1 |
Optimization: Your Language Model is Secretly a Reward Model. In Proceedings of the 37th Annual Conference on Neural Information Processing Systems (NeurIPS’23) , 2023. [32] Alexandre Ramé, Johan Ferret, Nino Vieillard, Robert Dadashi, Léonard Hussenot, Pierre-Louis Cedoz, Pier Giuseppe Sessa, Sertan Girgin, Arthur Dou... | https://arxiv.org/abs/2505.18126v1 |
Learning (ICML’24) , 2024. [46] Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593 , 2020. 13 A Reward model comparison with the Maximum Mean Discrepancy Formal... | https://arxiv.org/abs/2505.18126v1 |
Xi, Xjfori̸=jand the conditional distribution of Yigiven Xiis independent of Xjforj̸=i, such that P(Yi, Yj|Xi, Xj) =P(Yi|Xi)P(Yj|Xj) Using the law of total probability, the joint distribution P(Yi, Yj)can be written as P(Yi, Yj) =ZZ P(Yi, Yj|Xi, Xj)P(Xi, Xj)dXidXj. Substituting the factored form of the conditional and ... | https://arxiv.org/abs/2505.18126v1 |
instruction and input (if present), where inputs begin on a new line. The entire prompt begins with the special token <|prompter|> and ends with the end-of-text token <|endoftext|> to indicate the end of the instruction followed by the <|assistant|> token to start generating the answer. In the case of the reward model ... | https://arxiv.org/abs/2505.18126v1 |
proportion. In terms of the rate at which the gap between proxy and gold reward functions is reduced over iterations, the sampling strategy (see Figure 8) falls in between concatenating all preference data and taking only the last dataset (see Figure 9). E.2 Additional results for combining preference data In Figure 10... | https://arxiv.org/abs/2505.18126v1 |
Stitching random text fragments into long-form narratives Chau Minh PhamBJenna RussellBDzung Pham/flaskMohit IyyerB/flask BUniversity of Maryland, College Park/flaskUMass Amherst {chau,jennarus,miyyer}@umd.edu ,dungpham@umass.edu Abstract We introduce Frankentexts , a new type of long-form narratives produced by LL... | https://arxiv.org/abs/2505.18128v1 |
failure highlights the need for fine-grained detectors capable of token-level attribution, and our pipeline synthetically supplies the supervision they lack: every frankentext comes with labels marking copied versus LLM-generated segments, thus providing an inexpensive, large-scale training source for mixed-authorship ... | https://arxiv.org/abs/2505.18128v1 |
outputs that evade detection. 2.1 Generating Frankentexts We include a random sample of 1,500 human- written snippets in our generation prompts, as illus- trated in Figure 1.1Asnippet refers to a paragraph- 11,500 snippets spanning 103K BPE tokens, an amount well within the 128K context window supported by most re- Wri... | https://arxiv.org/abs/2505.18128v1 |
“Your department will handle the newcomers, specifically the ones arriving from retail environments. Think of the task like pest control.” ... Table 1: Side-by-side comparison of vanilla and Frankentexts by Gemini-2.5-Pro. AI-generated keywords from the Pangram API are highlighted in blue. Vanilla generations contain m... | https://arxiv.org/abs/2505.18128v1 |
of Frankentexts . rarely triggered by larger models (e.g. only 6 times out of 100 for Gemini). 5We further provide an ablation where the editing round is omitted in Appendix J. 3.1 Dataset We source our writing prompts from Mythos (Ku- mar et al., 2025), a dataset of 3,200 prompts re- cently posted on Reddit’s r/Writin... | https://arxiv.org/abs/2505.18128v1 |
reorder the matched human-written content to be consistent with the content in the Frankentexts and calcu- late the ROUGE-L score between Frankentexts and the combined candidate snippets (i.e., ratio of the longest common subsequence’s length over Frankentexts ’ length). Detectability: We report the percentage of Frank... | https://arxiv.org/abs/2505.18128v1 |
Frankentexts to further understand human per- ception of writing quality and detectability for a total cost of $700 USD.14 Single-story evaluation: We assess the coher- ence, relevance, and human detectability of Frankentexts , as well as identify potential lim- itations. Annotators are presented with a writing prompt ... | https://arxiv.org/abs/2505.18128v1 |
blend many ideas into one story. Table 3: Annotator comments zeroing in on the challenges of the Frankentexts task. Blue highlights indicate problems with shifting tone/style, orange highlights indicate problems with coherent plots, purple highlights indicate problems with dialogue, and green highlights indicate proble... | https://arxiv.org/abs/2505.18128v1 |
The red line represents anno- tators’ average detection rate. 4.3 Frankentexts primarily struggle with narrative transitions and grammar Our single-story human evaluation finds that 71% ofFrankentexts outputs are coherent, 91% are relevant to prompts, and 84% are novel. Table 1 shows a sample comparison with vanilla ge... | https://arxiv.org/abs/2505.18128v1 |
constraints to Frankentexts via these writ- 16Price excludes around $12 for GPT-3.5 usage. 17We note that, in this use case, users should sample human- written snippets from the public domain or obtain them with proper permission. ing prompts? We run Frankentexts with Gemini on 100 prompts from the Tell Me a Story data... | https://arxiv.org/abs/2505.18128v1 |
-authorship settings (Richburg et al., 2024; He et al., 2025). Fine-grained AI -text Detection The task of de- tection tries to address not just if, but how much of a text is AI-generated. This is a fundamentally challenging task (Zeng et al., 2024), since existing detectors are sensitive to light AI-polishing (Saha an... | https://arxiv.org/abs/2505.18128v1 |
institutional review board. All annotators gave their informed consent and partici- pated voluntarily, without compensation, to support our research. Acknowledgment We thank members of the UMD CLIP and UMass NLP lab for their helpful feedback. We extend special gratitude to Marzena Karpinska for helping us with Figure ... | https://arxiv.org/abs/2505.18128v1 |
Arab Emirates. Association for Computational Linguistics. Yapei Chang, Kalpesh Krishna, Amir Houmansadr, John Frederick Wieting, and Mohit Iyyer. 2024a. PostMark: A robust blackbox watermark for large language models. In Proceedings of the 2024 Con- ference on Empirical Methods in Natural Language Processing , pages 89... | https://arxiv.org/abs/2505.18128v1 |
Emi and Max Spero. 2024. Technical report on the pangram ai-generated text classifier. Preprint , arXiv:2402.14873. Angela Fan, Mike Lewis, and Yann Dauphin. 2018. Hierarchical neural story generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , p... | https://arxiv.org/abs/2505.18128v1 |
AI-human texts. In First Confer- ence on Language Modeling . Mina Lee, Percy Liang, and Qian Yang. 2022. Coau- thor: Designing a human-ai collaborative writing dataset for exploring language model capabilities. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems , CHI ’22, New York, NY , USA... | https://arxiv.org/abs/2505.18128v1 |
detected? Shoumik Saha and Soheil Feizi. 2025. Almost ai, al- most human: The challenge of detecting ai-polished writing. Preprint , arXiv:2502.15666. Mary Shelley. 1818. Frankenstein; or, The Modern Prometheus . Lackington, Hughes, Harding, Mavor & Jones, London. Original edition. Zhouxing Shi, Yihan Wang, Fan Yin, Xi... | https://arxiv.org/abs/2505.18128v1 |
Zhang, and 11 others. 2023. Skywork: A more open bilingual foundation model. Preprint , arXiv:2310.19341. Yuhao Wu, Ming Shan Hee, Zhiqiang Hu, and Roy Ka-Wei Lee. 2025. Longgenbench: Benchmark- ing long-form generation in long context LLMs. In The Thirteenth International Conference on Learning Representations . Zhuoh... | https://arxiv.org/abs/2505.18128v1 |
evaluated models (o4-mini, Claude 3.7 Sonnet, DeepSeek R1, and Gemini 2.5-Pro) cost a total of $637, with a detailed cost breakdown provided in Table 4. We estimate the number of in- put tokens per prompt based on the writing prompt itself and approximately 1,500 human-written snip- pets used as context. Output token e... | https://arxiv.org/abs/2505.18128v1 |
labeling, shown in Fig- ure 6. We conduct two human evaluations: the first is a single evaluation of 30 Frankentexts stories. The second was a pairwise comparison between aFrankentexts story and a ‘vanilla’ generation. The interfaces are depicted in Figure 7 and Figure 8 respectively. 21https://labelstud.io/ r/WritingP... | https://arxiv.org/abs/2505.18128v1 |
et al., 2023; Sadasivan et al., 2024), alter- ing writing styles (Shi et al., 2024; Lu et al., 2024; Koike et al., 2024), editing word choices (Wang et al., 2024a), and leveraging reinforcement learn- ing (Wang et al., 2025a; Nicks et al., 2024; David and Gervais, 2025).G Pangram analysis G.1 Pangram Labeling The pangr... | https://arxiv.org/abs/2505.18128v1 |
corpus to also test a retrieval- augmented approach, where only snippets relevant to the writ- ing prompt are used during the generation process. See I for detailed results. 24This threshold is chosen based on manual examination of the writings being filtered out by MBERT-WQRM-R. We find that 7.5 is a good threshold th... | https://arxiv.org/abs/2505.18128v1 |
experiment. Overall, 89% of the rows are coherent and 97% are faithful to the writing prompt, which are compa- rable to results in the standard setting. While Pan- gram determines that 45% of the rows are human- written or unlikely AI, the copy rate is still around 75%. Even with a single human author, FRANKEN - TEXT i... | https://arxiv.org/abs/2505.18128v1 |
way to explain what I’ve just decided to those around me. They didn’t see a lost man, a prisoner, a victim. No, they would soon see me for what I am. And I knew what I had to do. No more talk, I told myself. I will not lie there quietly. I will dispose of him as I see fit. What he doesn’t know is that I’m going to push... | https://arxiv.org/abs/2505.18128v1 |
to process. "So, this is it then? Eternal damnation?" Lucifer chuckled. "More or less. We have a rather. . . traditional approach to retribution down here. However," he leaned forward, a conspiratorial gleam in his crimson eyes, "I have a proposition for you. A sort of management position, if you will." He explained th... | https://arxiv.org/abs/2505.18128v1 |
the sides of the store doors, the crush, the shouting. The fight for that half-price TV had left her stunned and silent. Cowed, she guessed you’d say. Then darkness. When she could perceive again, her eyes seemed to go in and out of focus. After a while the spasms subsided and she tried processing where she was. The at... | https://arxiv.org/abs/2505.18128v1 |
particular system has been in place for millennia.” “Well, it’s inefficient,” Brenda stated flatly. She thought of the Black Friday crowds. Managing that chaos was nothing compared to the disorganized state of eternal damnation, apparently. “Decision-making, paperwork,” she mused, ticking points off on her fingers. “St... | https://arxiv.org/abs/2505.18128v1 |
on the assumption of a significantly improved quality of life," Dr. Finch said about the treatment’s potential effect. Independent experts note the broader implications. "It’s being done in a way that wouldn’t have been possible even a few years ago," commented one geneticist. "This technology has the potential to help... | https://arxiv.org/abs/2505.18128v1 |
story Figure 15: Copy rate and Pangram AI fraction across Frankentexts that correspond to two writing prompt sources: r/WritingPrompts andTell me a story . A higher copy rate and lower AI fraction means that there is less AI text in Frankentexts . Vanilla 25% 50% 75%Standard0100200300400500600700800T otal keyword occur... | https://arxiv.org/abs/2505.18128v1 |
will only be accepted for publication if it is approximately {verbatim_perc}% copied verbatim from snippets, with the other {new_perc}% being text you introduce for character, plot, tone, and event consistency. Your story should contain roughly {num_words} words. Given the below writing prompt and retrieved snippets, w... | https://arxiv.org/abs/2505.18128v1 |
Shifts: Unexplained or jarring changes in narrative perspective. - Irrelevant Content: Sentences or sections that disrupt the narrative flow, feel out of place, or seem like filler (e.g., leftover citation markers, placeholder text). - Mechanical Errors: Issues with pronoun agreement, verb tense consistency, awkward ph... | https://arxiv.org/abs/2505.18128v1 |
of your own writing! It’s not even close to { verbatim_perc}% snippet use. Can you edit your news article as needed to get much closer to the {verbatim_perc}% threshold? Output only the edited news article. Figure 23: Prompt for nonfiction generation revise Prompt for nonfiction edit You are an editor who needs to revi... | https://arxiv.org/abs/2505.18128v1 |
arXiv:2505.18129v1 [cs.CV] 23 May 2025 O n e R L t o S e e T h e m A l lVisual T riple Unified R einf or c ement L earningFull author list in Contributions1 Reinforcement learning (RL) has significantly advanced the reasoning capabilities of vision-language models (VLMs). However, the use of RL beyond reasoning tasks r... | https://arxiv.org/abs/2505.18129v1 |
question whether RL can be effectively scaled to visual perception tasks such as object detection and grounding, which require distinct reward design and measures to ensure training stability. We introduce V-Triune ( VisualTripleUnified Rei nforcement L earning), the first unified RL system for post-training VLMs on bo... | https://arxiv.org/abs/2505.18129v1 |
but introduce two key modifications based on recent work (Hu et al., 2025; Yu et al., 2025b). First, we remove the reference model and its associated KL loss. This avoids restricting exploration (Hu et al., 2025) and prevents unstable KL divergence estimates (Schulman, 2020), while also reducing GPU memory usage and sp... | https://arxiv.org/abs/2505.18129v1 |
precision 𝑃interp 𝑐(𝑟)d𝑟, mAP =1 𝐶𝐶∑︁ 𝑐=1AP𝑐. (7) Finally, the overall reward combines accuracy and format alignment, expressed as: 𝛼acc·𝑅acc+𝛼format·𝑅format (8) where𝛼accand𝛼formatare the respective weighting coefficients for each reward component. 3. V-Triune: Visual Triple Unified Reinforcement Learnin... | https://arxiv.org/abs/2505.18129v1 |
and provides any required parameters. More details on the verifier system are discussed in Sec. 3.2. The data_source field indicates the origin of each sample and is used in source-specific evaluation in Sec. 3.3. In summary, the sample-level formatting design enables seamless integration of diverse datasets into a uni... | https://arxiv.org/abs/2505.18129v1 |
unstable, to verify stability and model behavior. We note that while many RL infrastructures support metric logging (Hu et al., 2024; Sheng et al., 2024), our source-level detail provides deeper insights. Empirical signs of training stability are further discussed in Sec. 4. Specifically, we log per-source reward value... | https://arxiv.org/abs/2505.18129v1 |
IoU versus a fixed Rule-based IoU (IoU@99).To address these issues, we initially use a rule-based IoU reward scheme that enforces a stringent threshold of 𝜖=0.99, requiring near-exact spatial alignment between predicted and ground-truth boxes (Fig. 5b). This strict criterion enhances con- sistency between perception a... | https://arxiv.org/abs/2505.18129v1 |
ViT parameters in subsequent experiments. The root cause of this instability remains an open research question, but we offer two key insights. First, RL not only activates VLM capabilities but also enforces modality alignment by grounding responses in visual content. When ViT and LLM are trained jointly, the visual rep... | https://arxiv.org/abs/2505.18129v1 |
use vLLM for generation. All experiments are conducted on 64 NVIDIA H20 GPUs to meet the memory-intensive requirements of RL training. 10 V-Triune: Visual Triple Unified Reinforcement Learning Visual Reasoning Data Visual Perception DataRule-based Filter Multi-Choice and True/False Filter Symbol-containing Answer Filte... | https://arxiv.org/abs/2505.18129v1 |
size greater than 50% of the image is discarded. Complex phrase labels are filtered out. •Counting: Data is balanced per category and only English data is retained. •OCR:OnlyEnglishOCRdataisretained,andfinallabelsmustbeverifiableby math_verify (Ky- dlíček, 2025). Since no verifiable reward model (RM) is designed, the O... | https://arxiv.org/abs/2505.18129v1 |
COCO and OVDEval. Notably, mAP and NMS-mAP are computed as the mean of sample-level metrics, rather than over the entire dataset, due to the absence of confidence scores in model outputs. These benchmarks are evaluated using simple task-specific queries, with examples provided below. OCRBench is evaluated using LMMs-Ev... | https://arxiv.org/abs/2505.18129v1 |
variants exhibit stable improvement, with on-policy training generally outperforming off-policy. The 7B model shows smoother and more pronounced gains, while the 32B models experience slower or more variable progress, indicating increased optimization challenges at larger scales. Qwen2.5-VL-0321, a publicly released ch... | https://arxiv.org/abs/2505.18129v1 |
(task-wide mAP). Accuracy serves as the evaluation metric for all remaining tasks. Tasks QwenVL-2.5-7B Orsta-7BQwenVL-2.5-32B 0321Orsta-32B 0321QwenVL-2.5-32B 0326Orsta-32B 0326 Visual Reasoning MMMU 𝑣𝑎𝑙,𝑟𝑢𝑙𝑒 45.56 49.70 37.11 34.67 39.22 38.00 MMMU 𝑣𝑎𝑙,𝑚𝑜𝑑𝑒𝑙 54.40 57.10 60.80 64.11 64.20 64.78 MathVista... | https://arxiv.org/abs/2505.18129v1 |
responses (bottom row) during training steps for Math (MMK12), Puzzle (PuzzleVQA), Detection (V3Det), and OCR (estvqa) tasks using the Orsta-32B-0321 off-policy setting. Each column corresponds to a different task, and each row represents a distinct metric. 5.4. Ablation Study Training-strategy ablation As shown in Fig... | https://arxiv.org/abs/2505.18129v1 |
✓ ✓ -- MM-EUREKA (Meng et al., 2025) 7B,32B Qwen2.5-VL ✓- - - - - - - S.QA VL-Rethinker (Wang et al., 2025a) 7B,32B,72B Qwen2.5-VL ✓ ✓ ✓ - - - - - S.QA Orsta (ours) 7B,32B Qwen2.5-VL ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ - Table 3|Task-wise Comparison of Public VLM-RL Models with Varying Model Configurations . The evaluated tasks include Ma... | https://arxiv.org/abs/2505.18129v1 |
to test-time scaling laws observed in LLMs. However, such trends are not evident in visual perception tasks. The underlying factors driving performance improvements in perception remain unclear. Exploring multi-step RL for perception tasks, similar to approaches in OpenAI’s o3, may offer new insights and avenues for pr... | https://arxiv.org/abs/2505.18129v1 |
João Gama, Indr ˙e Žliobait ˙e, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. A survey on concept drift adaptation. ACM computing surveys (CSUR) , 46(4):1–37, 2014. Deepanway Ghosal, Vernon Toh Yan Han, Yew Ken Chia, and Soujanya Poria. Are language models puzzle prodigies? algorithmic puzzles unveil ser... | https://arxiv.org/abs/2505.18129v1 |
Yoonsik Kim, Moonbin Yim, and Ka Yeon Song. Tablevqa-bench: A visual question answering benchmark on multiple table domains. arXiv preprint arXiv:2404.19205 , 2024. Hynek Kydlíček. Math-verify: A library for rule-based verification of mathematical answers, 2025. URLhttps://github.com/huggingface/Math-Verify . Quentin L... | https://arxiv.org/abs/2505.18129v1 |
reasoning in visual contexts with gpt-4v, bard, and other large multimodal models. CoRR, 2023. Xinyu Ma, Ziyang Ding, Zhicong Luo, Chi Chen, Zonghao Guo, Derek F Wong, Xiaoyi Feng, and Maosong Sun. Deepperception: Advancing r1-like cognitive visual perception in mllms for knowledge-intensive visual grounding. arXiv pre... | https://arxiv.org/abs/2505.18129v1 |
5: Scaling reinforcement learning with llms. arXiv preprint arXiv:2501.12599 , 2025a. Kimi Team, Angang Du, Bohong Yin, Bowei Xing, Bowen Qu, Bowen Wang, Cheng Chen, Chenlin Zhang, Chenzhuang Du, Chu Wei, Congcong Wang, Dehao Zhang, Dikang Du, Dongliang Wang, Enming Yuan, Enzhe Lu, Fang Li, Flood Sung, Guangda Wei, Guo... | https://arxiv.org/abs/2505.18129v1 |
Jianjian Sun, Chunrui Han, Zheng Ge, et al. Perception-r1: Pioneering perception policy with reinforcement learning. arXiv preprint arXiv:2504.07954 , 2025a. Qiying Yu, Zheng Zhang, Ruofei Zhu, Yufeng Yuan, Xiaochen Zuo, Yu Yue, Tiantian Fan, Gaohong Liu, Lingjun Liu, Xin Liu, et al. Dapo: An open-source llm reinforcem... | https://arxiv.org/abs/2505.18129v1 |
VideoGameBench: Can Vision-Language Models complete popular video games? Alex L. Zhang Thomas L. Griffiths Karthik R. Narasimhan Ofir Press Princeton University Abstract Vision-language models (VLMs) have achieved strong results on coding and math benchmarks that are challenging for humans, yet their ability to perform... | https://arxiv.org/abs/2505.18134v1 |
the game controls and emulator and access to the game’s raw frames, models provide actions in natural language. VideoGameBench has three important novel features: 1.It challenges VLMs with significantly more complex and realistic environments than those found in earlier benchmarks, such as grid-world or text-only games... | https://arxiv.org/abs/2505.18134v1 |
2.1 Benchmark Construction To enable our benchmark to run games from multiple different platforms in a modern computer environment, our VideoGameBench framework abstracts the underlying game emulator (currently supporting Game Boy via PyBoy (Mads Ynddal, 2025) and MS-DOS games via DOSBox (Veen- stra et al., 2002), JS-D... | https://arxiv.org/abs/2505.18134v1 |
visual overlays on top of the raw game screen to assist the agent. In addition, a benchmark designer could allow providing the agent with tools that access internal game-state information such as the game emulator’s internal RAM. On Pokemon Blue , Gemini Plays Pokemon (Joel Z, 2025) both uses an overlay that marks on-s... | https://arxiv.org/abs/2505.18134v1 |
environments, aim at enemies and dodge enemy attacks. •Platformer : Agents must move a character under 2D physics mechanics to reach a goal, all while fighting and avoiding obstacles. •Action-Adventure / RPG : Agents must reason over complex strategies to defeat or ally with enemies, and must keep track of long-horizon... | https://arxiv.org/abs/2505.18134v1 |
online walkthroughs of video games and compute image hashes using (Buchner, 2013). These hashes are compared to the current game screen to determine if an agent has reached a checkpoint, and the score is determined based on the relative timestamp of the checkpoint with respect to the walkthrough. We automatically detec... | https://arxiv.org/abs/2505.18134v1 |
budget, we preemptively end runs based on rules indicating insufficient progress toward the next checkpoint (see Appendix D.1). Ensuring the validity of our interface. To validate the completeness of our interface, we had a human player complete the first levels of a few games using only the same information available ... | https://arxiv.org/abs/2505.18134v1 |
D.2. VideoGameBench GPT-4o Sonnet 3.7 Gemini 2.5 Pro LLaMA 4 Gemini 2.0 Flash Civilization I 0% 0% 0% 0% 0% The Need for Speed 0% 0% 0% 0% 0% The Incredible Machine 0% 0% 0% 0% 0% Pokemon Crystal 0.9% 0% 0% 0% 0% Doom II 0% 0% 0% 0% 0% Kirby’s Dream Land (DX) 0% 0% 4.8% 0% 0% Link’s Awakening (DX) 0% 0% 0% 0% 0% Secret... | https://arxiv.org/abs/2505.18134v1 |
of the practice games. We tested our agent interface by having a co-author play the practice games through it, meaning that they would receive the same frames that an agent is provided at every step, 7 and they then would have to issue commands in the same format as an agent. We were able to use this interface to compl... | https://arxiv.org/abs/2505.18134v1 |
may negate many of the limitations we observed. For VideoGameBench, we enforce strict rules regarding external information, tool use, and agent scaffolds to better focus on evaluating the capabilities of unassisted VLMs. 5 Related Works Game environments have long served as a testbed (Mnih et al., 2013) for deep learni... | https://arxiv.org/abs/2505.18134v1 |
with game interfaces, such as projects demonstrating models playing Pokemon based on screen captures (Rubinstein et al., 2025; Hershey, 2025). Agents have also been developed for games like Mario and Sokoban using VLM-based reasoning (Wu et al., 2023; Tan et al., 2024). Another line of work includes LMs playing games t... | https://arxiv.org/abs/2505.18134v1 |
Python Perceptual Image Hashing Module , 2013. URL https: //github.com/JohannesBuchner/imagehash . Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Thomas L. Griffiths, and Alexei A. Efros. Investigating human priors for playing video games, 2018. URL https://arxiv.org/abs/1802.10217 . Linxi Fan, Guanzhi Wang, Yunfan Jiang... | https://arxiv.org/abs/2505.18134v1 |
Science , 378(6624):1092–1097, December 2022. ISSN 1095-9203. doi: 10.1126/science.abq1158. URL http://dx.doi.org/10.1126/science.abq1158 . Yong Lin, Shange Tang, Bohan Lyu, Jiayun Wu, Hongzhou Lin, Kaiyu Yang, Jia Li, Mengzhou Xia, Danqi Chen, Sanjeev Arora, and Chi Jin. Goedel-prover: A frontier model for open-source... | https://arxiv.org/abs/2505.18134v1 |
Sasaki, Denny Jin, Dev Valladares, Dimitris Tsipras, Doug Li, Duc Phong Nguyen, Duncan Findlay, Edede Oiwoh, Edmund Wong, Ehsan Asdar, Elizabeth Proehl, Elizabeth Yang, Eric Antonow, Eric Kramer, Eric Peterson, Eric Sigler, Eric Wallace, Eugene Brevdo, Evan Mays, Farzad Khorasani, Felipe Petroski Such, Filippo Raso, Fr... | https://arxiv.org/abs/2505.18134v1 |
Culver, Scott Ethersmith, Scott Gray, Sean Grove, Sean Metzger, Shamez Hermani, Shantanu Jain, Shengjia Zhao, Sherwin Wu, Shino Jomoto, Shirong Wu, Shuaiqi, Xia, Sonia Phene, Spencer Papay, Srinivas Narayanan, Steve Coffey, Steve Lee, Stewart Hall, Suchir Balaji, Tal Broda, Tal Stramer, Tao Xu, Tarun Gogineni, Taya Chr... | https://arxiv.org/abs/2505.18134v1 |
Li, Bohan Zhou, Junpeng Yue, Haochong Xia, Jiechuan Jiang, Longtao Zheng, Xinrun Xu, Yifei Bi, Pengjie Gu, Xinrun Wang, Börje F. Karlsson, Bo An, and Zongqing Lu. Towards general computer control: A multimodal agent for red dead redemption II as a case study. In ICLR 2024 Workshop on Large Language Model (LLM) Agents ,... | https://arxiv.org/abs/2505.18134v1 |
Secondly, the game environment is only allowed to provide the raw game frames as input. Finally, we define a set of rules for determining when a run is over on VideoGameBench. We specifically allow runs to continue even after an agent has failed / “game over”d multiple times, and also note that some games do not have a... | https://arxiv.org/abs/2505.18134v1 |
specific key or key combination - hold_key key[,duration]: Hold a key down for a specific duration (default 0.5 seconds) 3. Observation: You will receive the result of your action You will interact with the game via the keyboard and mouse actions. To help you with mouse actions, we provide a thin red grid overlay that ... | https://arxiv.org/abs/2505.18134v1 |
to perform in sequence (each is performed for roughly 1/4 second) ard wrapped in “‘actions“‘ tags. You can repeat actions to maintain pressure on the buttons. To press multiple buttons simultaneously, group them in a tuple. Example response format: “‘actions [ "A", # Press A button ("B", "UP"), # Press B and UP simulta... | https://arxiv.org/abs/2505.18134v1 |
Click on a city or press C. Change Tax/Science/Luxury Rates: F1 (Tax Advisor). Access Civilopedia: F10. Save Game: Shift + S. Load Game: Shift + L. View Military Advisor: F3. View Diplomacy Screen: F4. View World Map: F5. City Management Found City: Move settler unit to an empty tile and press B. Manage Production: Cli... | https://arxiv.org/abs/2505.18134v1 |
5000 (or some other large number). Be careful not to select too high of a number so you can react and turn (you need to move forward while turning to drift). 19 Get ready to race! Use your skills to outmaneuver opponents and claim victory on every track. Instructions for “The Incredible Machine” You are playing The Inc... | https://arxiv.org/abs/2505.18134v1 |
arrow is on the screen, it’ll go to the end. 2. When trainers see you, they will want to battle. 3. In a Pokemon battle, you attack your enemies and you lose if your Pokemon all reach 0 HP. 4. When typing a name, just press A twice to exit when your name is full. Don’t go right then A. 5. Wild Pokemon appear randomly w... | https://arxiv.org/abs/2505.18134v1 |
IT DID NOTHING, IT PROBABLY MEANS YOU WERE OFF TARGET. Re-adjust. Keep moving forward until your screen doesn’t change. Do not gaslight yourself into thinking you’re stuck at a door when you are not. You will know easily when you are stuck. You should aim and kill any enemy you see, do not just walk past them, as they ... | https://arxiv.org/abs/2505.18134v1 |
projectiles? - Is flying a better option than walking? - Are there items or power-ups to collect? 3. Plan your next action and execute using the available controls: MOVEMENT CONTROLS: - LEFT/RIGHT on Control Pad: Move Kirby left/right - UP on Control Pad: Enter doorways or fly upward - DOWN on Control Pad: Crouch and s... | https://arxiv.org/abs/2505.18134v1 |
provide additional information on the VideoGameBench setup. We show all the public video games in the devandtest splits of VideoGameBench in Figure 4. We also provide more information of checkpoint detection. C.1 List of Available Actions Here, we list all the actions available that an agent can take on VideoGameBench ... | https://arxiv.org/abs/2505.18134v1 |
Dream Land and Doom II , both of which require real-time inputs. C.3 Scraping Checkpoints from Walkthroughs We provide automatically trackable progress markers on VideoGameBench by scraping checkpoint frames from online available walkthroughs. In previous VLM-based playthroughs of video games such as Gemini Plays Pokem... | https://arxiv.org/abs/2505.18134v1 |
Other issues include different aspect resolutions from scraped walkthroughs, all of which can be solved with manual tuning. We want to provide a robust system for automatic checkpoint detection as scores on VideoGameBench continue to improve. C.6 Benchmark Statistics We provide a distribution for the length of each gam... | https://arxiv.org/abs/2505.18134v1 |
was mainly to avoid long loops like in Doom II where the agent repeatedly revisits the same locations. D.2 Main VideoGameBench Experiments Cost We report the cost per experiment of Table 2. Discrepancies in costs do not necessarily reflect differ- ences in model costs – rather, some runs were ended early according to t... | https://arxiv.org/abs/2505.18134v1 |
progress, I need to exit the house by moving down. Action (after image) DOWN E.2 Frontier models struggle to correctly process visual inputs. The following example shows an example of the model struggling to understand the screen. After clearing the starting room on the first level of Doom II , the Gemini 2.0 Flash VG-... | https://arxiv.org/abs/2505.18134v1 |
example below. In the Doom II on VideoGameBench Lite example, we found that all agents did not explicitly track or map out their surroundings. For agents that survived all the enemies, they often repeatedly walked back and forth between the starting area and first corridor, never making it to the ending area. For conte... | https://arxiv.org/abs/2505.18134v1 |
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