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training strategies for efficient embodied reasoning training strategies for efficient embodied reasoning william chen 1 suneel belkhale 2 suvir mirchandani 2 oier mees 1 danny driess 3 karl pertsch 1 sergey levine 1 1 uc berkeley 2 stanford university 3 physical intelligence paper data ecot lite enjoys the performance benefits of embodied reasoning without the test time computational cost abstract robot chain of thought reasoning cot wherein a model predicts helpful intermediate representations before choosing actions provides an effective method for improving the generalization and performance of robot policies especially vision language action models vlas while such approaches have been shown to improve performance and generalization they suffer from core limitations like needing specialized robot reasoning data and slow inference speeds to design new robot reasoning approaches that address these issues a more complete characterization of why reasoning helps policy performance is critical we hypothesize several mechanisms by which robot reasoning improves policies 1 better representation learning 2 improved learning curricularization and 3 increased expressivity we then devise simple variants of robot cot reasoning to isolate and test each one we find that learning to generate reasonings does lead to better vla representations while attending to the reasonings aids in actually leveraging these features for improved action prediction our results provide us with a better understanding of why cot reasoning helps vlas which we use to introduce two simple and lightweight alternative recipes for robot reasoning our proposed approaches achieve significant performance gains over non reasoning policies state of the art results on the libero 90 benchmark and a 3x inference speedup compared to standard robot reasoning tldr and takeaways we test various hypotheses of why embodied chain of thought reasoning ecot improves robot policy performance to extract insights for developing better robot reasoning approaches our results mainly support the hypothesis that reasoning improves representation learning this contrasts with the expressivity arguments usually made for why reasoning helps llms we introduce two approaches for learning representations from reasonings without producing them at test time which we call ecot lite we validate their effectiveness in the libero simulator and real world bridge manipulation tasks we find ecot lite is faster than standard ecot while being more performant than standard vlas even slightly surpassing state of the art performance in libero why does reasoning improve policy performance hypotheses to develop better embodied robot reasoning methods we first need to understand why reasoning helps robot policies in the first place we present three hypotheses for why this might be the case hyp 1 better representation learning the reasoning contains task relevant features so learning to reason embeds this information in the model s representations even if reasonings are not generated at test time hyp 2 improved learning curricularization having reasonings in context during training indicates the important features for choosing actions which must otherwise be learned from scratch hyp 3 increased expressivity more test time in context tokens improves the model s expressivity even if the tokens lack semantic meaning policy variants schematic illustrating all policy variants for testing our three hypotheses we create simple variants of embodied chain of thought reasoning ecot to isolate and test each of these hypotheses reasoning pre training and co training rather than learning to generate reasonings and actions in the same context the policy can be trained on reasonings and actions separately in pre training the policy is first trained exclusively to reason then fine tuned exclusively on actions in co training the policy is trained to reason and act in separate datapoints within the same batch reasoning dropout the model is trained to generate reasonings and actions in sequence as with standard ecot but the reasonings are randomly dropped out at test time the reasonings can be turned on and off as needed reasoning scaffolding the policy has reasonings in context during training but no loss is assigned to it the policy cannot generate reasonings only attend to it this is akin to teacher student learning wherein the policy access oracle features during training only thinking tokens the policy s expressivity is improved by adding many meaningless thinking tokens such approaches have been used in other domains too experiments and results our ecot lite policies outperform standard vlas in both simulation and real world evaluations while being significantly faster than standard ecot we train vlas using all the above recipes as well as both an ecot and standard non reasoning vla baseline all using the minivla architecture and codebase we evaluate all these policies on the libero 90 simulated manipulation benchmark we find that the most performant variants are reasoning dropout and reasoning pre training which we call ecot lite we then reproduce and validate these approaches with openvla based policies on real world bridge widowx manipulation tasks libero 90 experiments we find that both full ecot and our reasoning dropout policy achieve the best performance on libero 90 90 8 and 89 4 respectively slightly outperforming past state of the art 88 6 the reasoning pre training policy comes close 87 1 as well notably both these methods do not produce test time reasonings making them much faster than ecot in contrast reasoning co training is less effective than these two approaches 84 2 even though it likewise learns representations from reasonings this suggests that not all representation learning approaches are equally conducive for positive transfer between the reasoning and action prediction tasks nevertheless co training outperforms the non reasoning standard vla baseline 82 0 this all strongly supports hyp 1 we also find that reasoning scaffolding performs comparably to co training 84 1 in weak support of hyp 2 however thinking tokens performs worse than the standard vla 78 9 79 8 which is evidence against hyp 3 this suggests expanded expressivity is not helpful for robot reasoning even when using a small 1b parameter minivla and that the reasoning tokens must be meaningful to be useful these analyses are repeated in harder variants of the libero 90 benchmark wherein we perturb the task objects starting locations and add distractors the above trends hold for these challenge splits as well bridge experiments we find that reasoning dropout and reasoning pre training are effective for real world manipulation tasks conducted in the bridge widowx environment as well as with libero both approaches similarly improve over the standard bc recipe while being around 3x faster than ecot many of the common failure modes of the reasoning dropout policy in bridge seem to be fixed by having test time reasonings curiously reasoning pre training is more performant than reasoning dropout in bridge reversing the trend seen in libero we suspect this is because reasoning dropout is more effective in narrower domains many of said policy s failure modes seem to be intuitively solved by turning on test time reasonings however reasoning pre training maintains good generalization performance it matches or outperforms standard ecot in all but one semantic generalization bridge task which robot reasoning approach is best for my problem based on our findings we make prescriptions for when to use each of the above robot reasoning approaches standard embodied chain of thought reasoning policies are the most performant but they are significantly slower than the other approaches they are best for tasks where performance is critical and speed is not a concern as both ecot lite strategies do not generate test time reasonings they are much faster matching the inference speeds of standard vla policies reasoning pre training seems more effective in diverse domains it matches or surpasses the semantic generalization capabilities of ecot in all but one bridge task as the approach involves tuning on just reasonings and just actions separately it also does not require paired embodied reasoning data however by needing to train on these tasks sequentially it needs more consecutive gradient steps to expose the policy to an equal amount of reasoning and action data finally reasoning dropout seems more performant in narrow domains like libero where it matches full ecot and surpasses state of the art performance additionally its training recipe is almost identical to standard ecot the only difference is that reasonings are occasionally dropped during training thus they allow users to turn test time reasonings off and on as needed bibtex article chen25 ecot lite title training strategies for efficient embodied reasoning author william chen and suneel belkhale and suvir mirchandani and oier mees and danny driess and karl pertsch and sergey levine journal arxiv preprint arxiv 2505 08243 year 2025 website borrowed from nerfies under a creative commons attribution sharealike 4 0 international