In-Context Reinforcement Learning for Tool Use in Large Language Models YaoqiYe*1 YiranZhao*2 KeyuDuan1 ZeyuZheng3 KenjiKawaguchi1 CihangXie4 MichaelQizheShieh1 | | | | Abstract | | | | 1.Introduction | | | | | | | | | --- | --- | --- | -------- | --- | --- | --- | --------------- | --- | -------- | -------- | --- | ------ | ------ | ---- | | | | | | | | | Recent advances | | in large | language | | models | (LLMs) | (Guo | etal.,2025;Yangetal.,2025;Seedetal.,2025;Teametal., | While | large | language | | models | (LLMs) | exhibit | | | | | | | | | | ----- | ----- | -------- | --- | ------ | ------ | ------- | --- | --- | --- | --- | --- | --- | --- | --- | 2025)haveshowntheireffectivenessinaddressingawide strongreasoningabilities,theirperformanceon 6202 raM 9 ]IA.sc[ 1v86080.3062:viXra complextasksisoftenconstrainedbythelimita- range of complex tasks (Wang et al., 2024a; Hsiao et al., | | | | | | | | 2025;Shietal.,2025;Quetal.,2025). | | | | | Nevertheless,akey | | | | ------------------------------ | --- | ----------- | --- | -------------- | ----------- | ---------- | --------------------------------- | -------- | ------ | ------------ | ---- | ----------------- | ------------ | --- | | tionsoftheirinternalknowledge. | | | | | Acompelling | | | | | | | | | | | | | | | | | | limitation | remains: | these | models | rely | on | a fixed body | of | | approach | | to overcome | | this challenge | | is to aug- | | | | | | | | | | | | | | | | | knowledge | acquired | during | pretraining, | | which | inherently | | mentthesemodelswithexternaltools—suchas | | | | | | | | restricts their | ability | to | adapt | to new | or time-sensitive | | in- | | ------ | ------------ | --- | --- | ------------ | --- | -------- | --------------- | ------- | --- | ----- | ------ | ----------------- | --- | --- | | Python | interpreters | | for | mathematical | | computa- | | | | | | | | | formation(Gaoetal.,2023;Zhuetal.,2025;Wangetal., tionsorsearchenginesforretrievingfactualin- | | | | | | | | 2024b; Cheng | et | al., 2024; | Matarazzo | | & | Torlone, | 2025). | | ---------- | --- | -------- | --- | -------- | ------ | ------ | ------------ | --- | ---------- | --------- | --- | --- | -------- | ------ | | formation. | | However, | | enabling | models | to use | | | | | | | | | Tomitigatethisissueandenhancemodelflexibility,recent thesetoolseffectivelyremainsasignificantchal- researchhasfocusedonenablingLLMstointeractwithex- | lenge. | Existing | methods | | typically | rely | on cold- | | | | | | | | | | ------ | --------- | ------- | ----- | --------- | ---------- | -------- | --------------------------- | --- | --- | ------------------------- | --- | --- | --- | --- | | | | | | | | | ternaltoolsduringinference. | | | Thisincludesgeneratingand | | | | | | start | pipelines | that | begin | with | supervised | fine- | | | | | | | | | executingPythoncodeformathematicalreasoning,lever- tuning(SFT),followedbyreinforcementlearning agingwebsearchenginestoaccessup-to-dateanddomain- (RL).Theseapproachesoftenrequiresubstantial specificcontent,andinvokingdedicatedhelpermodelsfor | amounts | of | labeled | data | for SFT, | which | is ex- | | | | | | | | | | ------- | --- | ------- | ---- | -------- | ----- | ------ | --- | --- | --- | --- | --- | --- | --- | --- | pensivetoannotateorsynthesize. Inthiswork, specializedsubtasks(Guoetal.,2024;Teametal.,2025;Li etal.,2025b;Jinetal.,2025a;Fengetal.,2025). weproposeIn-ContextReinforcementLearning | (ICRL), | an | RL-only | framework | | that | eliminates | | | | | | | | | | ------- | --- | ------- | --------- | --- | ---- | ---------- | ------------ | -------- | --- | --------- | --- | ---- | ------ | ------ | | | | | | | | | The dominant | training | | paradigms | for | LLMs | either | lever- | theneedforSFTbyleveragingfew-shotprompt- agereinforcementlearning(RL)withverifiablerewardsig- ing during the rollout stage of RL. Specifically, nals(Guoetal.,2025;Jinetal.,2025a;Zhaoetal.,2025), ICRLintroducesin-contextexampleswithinthe | | | | | | | | or adopt | a cold-start | strategy | | that begins | with | supervised | | | --- | --- | --- | --- | --- | --- | --- | -------- | ------------ | -------- | --- | ----------- | ---- | ---------- | --- | rollout prompts to teach the model how to in- fine-tuning(SFT)followedbyanRLphase(Meietal.,2025; | voke | external | tools. | Furthermore, | | as | training | | | | | | | | | | ---- | -------- | ------ | ------------ | --- | --- | -------- | ------------------ | --- | ----------------------------- | --- | --- | --- | --- | --- | | | | | | | | | Nguyenetal.,2025). | | DirectlyapplyingRLfromscratch | | | | | | progresses, the number of in-context examples often yields poor performance, as the model lacks initial isgraduallyreduced,eventuallyreachingazero- tool-useabilitiesandstruggleswithineffectiveexploration. shotsettingwherethemodellearnstocalltools WhileincorporatinganSFTstagecanguidethemodelto- independently.Weconductextensiveexperiments wardamorefavorableinitialization,ittypicallyrequiresa acrossarangeofreasoningandtool-usebench- largeamountofhigh-qualitylabeleddata,whichisexpen- ICRL marks. Results show that achieves state- sivetoannotateorsynthesize. | of-the-art | | performance, | | demonstrating | | its effec- | | | | | | | | | | ---------- | --- | ------------ | --- | ------------- | --- | ---------- | ------------- | --- | ------------ | --- | ---------- | --- | ------------- | --- | | | | | | | | | In this work, | | we introduce | | In-Context | | Reinforcement | | tivenessasascalable,data-efficientalternativeto | | | | | | 1 | | Learning | (ICRL), | a lightweight | | and | supervision-efficient | | | | --- | --- | --- | --- | --- | --- | --- | -------- | ------- | ------------- | --- | --- | --------------------- | --- | --- | traditionalSFT-basedpipelines. frameworkfortrainingLLMstoperformtool-augmented | | | | | | | | reasoning. | UnlikepriorapproachesthatrelyonSFT,ICRL | | | | | | | | --- | --- | --- | --- | --- | --- | --- | ---------- | --------------------------------------- | --- | --- | --- | --- | --- | --- | teachestoolusedirectlythroughRLrolloutsthatareaug- *Equal contribution 1National University of Singapore mentedwithin-contextdemonstrations. Specifically,during | 2Salesforce | AI | Research | 3University | | of California | Berkeley | | | | | | | | | | ----------- | --- | -------- | ----------- | --- | ------------- | -------- | --- | --- | --- | --- | --- | --- | --- | --- | RLtraining,weconstructeachrolloutpromptbyprepending | 4University | of California, | | Santa | Cruz. | Correspondence | to: Yi- | | | | | | | | | | ----------- | -------------- | --- | ----- | ----- | -------------- | ------- | --- | --- | --- | --- | --- | --- | --- | --- | asmallnumberoffew-shotexamplesthatillustratehowto | ran Zhao | , | | | | Michael | Qizhe Shieh | | | | | | | | | | -------- | -------------------------- | --- | --- | --- | ------- | ----------- | --- | --- | --- | --- | --- | --- | --- | --- | . reasonstep-by-step,invoketoolsinastructuredformat,and | | | | | | | | generatefinalanswers. | | Thesedemonstrationsserveassoft | | | | | | | --- | --- | --- | --- | --- | --- | --- | --------------------- | --- | ------------------------------ | --- | --- | --- | --- | --- | Preprint.March10,2026. supervisionduringexploration,guidingthemodeltoward | 1Code | | | | https://github.com/ | | | | | | | | | | | | ----- | --- | --- | --- | ------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | is publicly available at successfulbehaviorwithoutrequiringlabeledtrajectories. applese233/ICRL 1 In-ContextReinforcementLearningforToolUseinLargeLanguageModels Figure1.ICRLtrainingworkflow.Themodelistrainedthroughamulti-stagecurriculumthatgraduallyreducesthenumberofin-context examplesintherollouttemplate.Ateachstage,theLLMgeneratestool-augmentedrollouts,receivesrewards,andupdatesitspolicyvia reinforcementlearning,enablingatransitionfromimitationtoautonomoustooluse. Furthermore,astrainingprogresses,wegraduallyreducethe TheseresultshighlightICRL’sabilitytogeneralizetodi- numberofdemonstrationsincludedintheserolloutprompts, versetool-augmentedreasoningdomainsanditspotentialas transitioningthemodelfromfew-shottozero-shotsettings. aunified,scalableframeworkfortrainingtool-usingmodels Thisprogressivereductionformsacurriculumthatencour- withoutcostlysupervision. agesthemodeltointernalizetool-usestrategiesandproduce structuredoutputsautonomously,withoutrelyingonprompt- 2.In-ContextReinforcementLearning(ICRL) | basedscaffolding. | WeoptimizethemodelusingRLwitha | | | | | | | | | | ----------------- | ------------------------------ | --- | --- | --- | --- | --- | --- | --- | --- | rewardthatbalancestaskaccuracyandformatcorrectness. In this section, we formally introduce tool use in LLMs Toensurestability,weadoptGRPO(Shaoetal.,2024)with anddescribehowRLcanbeappliedtotrainsuchbehavior. lossmaskingtoignorenon-trainabletooloutputs. Byem- WealsopresenttheoverallworkflowofICRL,detailingits beddingandgraduallyremovingdemonstrationsfromthe trainingtemplates,learningprocess,andrewarddesign. RLrollouts,ICRLmergestheefficiencyofpromptingwith theadaptabilityofRL,offeringascalable,supervision-light 2.1.ToolUseinLLMs alternativetotraditionalSFT+RLpipelines. | | | | | When | LLMs encounter | queries | that exceed | the | scope of | | --- | --- | --- | --- | ---- | -------------- | ------- | ----------- | --- | -------- | Weconductcomprehensiveexperimentsacrossarangeof theirinternalknowledge,theymustleverageexternaltools QA and reasoning benchmarks to evaluate the effective- to obtain updated information or perform more complex nessofICRL.Withoutrelyingonsupervisedfine-tuningor | | | | | reasoning. | Forexample,searchenginescanprovideaccess | | | | | | --- | --- | --- | --- | ---------- | ---------------------------------------- | --- | --- | --- | --- | ground-truthtooltraces,ICRLachievesstate-of-the-artper- torecentknowledge,whilePythoninterpreterscanbeused formanceonchallengingQAdatasets,outperformingstrong toexecutestructuredreasoningprocedures. | baselines such | as ZeroSearch | (Sun et al., | 2025), Search- | | | | | | | | -------------- | ------------- | ------------ | -------------- | --- | --- | --- | --- | --- | --- | Formally,givenaqueryqandanexternaltoolT,themodel R1(Jinetal.,2025a),andParallelSearch(Zhaoetal.,2025) | | | | | generates | a response | y = | (y ,y ,...,y | ), where | each | | ---------------------------------------------- | --- | --- | --- | --------- | -------------- | -------- | ------------ | --------- | -------- | | byupto8.9onQwen2.5-3B(Yangetal.,2024b)and7.3on | | | | | | | 1 2 | |y| | | | | | | | token | is conditioned | not only | on the | query and | previous | Qwen2.5-7Binaverageexactmatchaccuracy.Thegainsare tokens,butalsoonahistoryofpriorinteractionswiththe especiallypronouncedonmulti-hopreasoningtasks,where ICRLachievesdouble-digitimprovementsondatasetslike tool. Thisdefinesaconditionaldistributionoftheform: | TriviaQA(Joshietal.,2017),2Wiki(Hoetal.,2020),and | | | | | | |y| | | | | | ------------------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | Musique(Trivedietal.,2022). Furthermore,incontrastto (cid:89) | | | | | | π θ (y |q,T)= | | π θ (y t |y | ..., | | | a search | query | | | | | | | | as ..., a retrieved information andr (θ)=π (τ |q,τ )/π (τ |q,τ ). | | | | | | | | i,t | | θ i,t | i,...,andafinal | answeras.... | | | | | | | 2.3.ICRL | | | | | | | | ----------------------------- | --- | --- | --- | --- | --- | --- | -------- | --- | --- | --- | --- | --- | --- | Furthermore,thetoolfunctionsasaresponsemechanism. Training Process. Rather than training models from Forexample,asearchenginecanbemodeledasaretrieval scratchusingreinforcementlearning,whichoftensuffers | | | V∗ | V∗, | V∗ | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | function T : → where is the space of textual fromsparserewardsandinefficientexploration,orrelying sequences. Givenasearchqueryq′,thetoolreturnsanob- exclusivelyonfew-shotprompting,whichincurssubstantial servationo=T(q′),suchasthetop-kdocumentsretrieved inferenceoverhead,weintroduceICRL,aframeworkthat fromacorpus. Thisobservationisappendedtothemodel’s integrates the strengths of both approaches. ICRL lever- contextandusedinsubsequentgenerationsteps. agesthesampleefficiencyandinductivebiasoffew-shot promptingwhilebenefitingfromtheexplorationcapabilities | 2.2.RLwithToolUse | | | | | | | ofreinforcementlearning. | | | | | | | | ----------------- | --- | --- | --- | --- | --- | --- | ------------------------ | --- | --- | --- | --- | --- | --- | RL Objective Function. After formulating the tool- Atthebeginningoftraining,weincorporateasmallnumber augmentedreasoninginLLMsasaMarkovDecisionPro- of tool-use demonstrations into the model’s rollout tem- cess(MDP),wecandefinethecorrespondingreinforcement plate. These examples guide the model toward effective learning(RL)objectiveasfollows: tool-augmentedreasoningviain-contextlearning,akinto | | | | | | | | few-shotprompting. | | Theresultingpolicyisdenotedas: | | | | | | --- | --- | --------------- | --- | ------ | --- | --- | ------------------ | --- | ------------------------------ | --- | --- | --- | --- | | | max | E | [r | (q,y)] | | | | | | | | | | | | | q∼D,y∼πθ(·|q,T) | | ϕ | | | | | | | | | | πθ | | | | | | | (2) | | | | |y| | | | | | --- | --- | --- | ------------- | --- | ---------- | --- | ---- | --- | ------ | -------- | ------- | --- | --- | | | | −βD | [π (y |q,T)∥π | | (y |q,T)], | | | | | (cid:89) | | | | | | | KL | θ | ref | | | π (y | |P | ,q,T)= | π (y |P | ,y ,q,H | ), | (5) | | | | | | | | | θ | N | | θ t | N ...everytimeyougetnew information.Afterreasoning,ifyoufindyoulacksomeknowledge,youcancallasearchenginebyquery anditwillreturnresultsbetween....Youcansearchasmanytimesasyouwant.Finally, providetheanswerinside.... Herearesomeexamples: ExampleProblem:q demo ExampleSolution:...... ......a | (repeatedforN | | examples | | | | | | | | | | | | ------------- | --- | -------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | Nowsolvethefollowingproblem: ActualProblem:question | Table2.Formatviolationpenaltiesforcomputingreward | | | | | . | | | | | | | | | ------------------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | format Algorithm1ICRL | Violation | | | Rationale | | | | | | | | | | | --------- | --- | --- | --------- | --- | --- | ------ | ------- | -------- | ----------- | ----- | --- | ------ | | | | | | | | Input: | Initial | policy π | , reference | model | π | , tool | | | | | | | | | | θ | | | ref | | Notag Mustprovidestructuredanswer T, initial few-shot prompt P , dataset partitions N Unbalancedtags ProperXMLstructurerequired {D(N),D(N−1),...,D(0)}, reward function r (·), ϕ | Notag | | | Shoulddemonstratereasoning | | | | | | | | | | | --------------------- | --- | --- | -------------------------- | --- | --- | ---------------- | --- | --- | --- | --- | --- | --- | | Unbalancedtags | | | | | | numberofRLstepsT | | | | | | | ProperXMLstructurerequired Nousage Shouldutilizeavailabletool Output: Trainedmodelπ θ | Emptyanswercontent | | | Answermustbesubstantive | | | 1: fork=N | | to0do | | | | | | ------------------ | --- | --- | ----------------------- | --- | --- | --------- | ------- | ------------ | --- | ------ | ---- | --- | | | | | | | | 2: | // Step | 1: Construct | | prompt | with | | k demonstrations P ←selectkexamplesfromP | reward | = 1 if the | prediction | exactly | matches | the cor- | 3: | k | | | N | | | | ------------------------- | ---------- | ---------- | ------- | ------- | -------- | --- | ------------------------------------------ | --- | --- | --- | --- | --- | | | acc | | | | | | D(k)←RLtrainingsubsetforcurrentpromptlevel | | | | | | | rectanswer,and0otherwise. | | | | | | 4: | | | | | | | | | | | | | | 5: | fort=1toT | do | | | | | The reward component evaluates the model’s adher- 6: forq∈D(k)do format | encetotheexpectedstructuredoutputformat,specifically | | | | | | 7: | π | ←π | | | | | | ---------------------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | θold θ | | | | | | | 8: | Sample | N trajectories | | {τ | ,...,τ | } ∼ | | ----------------------- | --- | --- | -------------- | --- | --- | --- | ------ | -------------- | --- | --- | ------ | --- | | thecorrectuseofXMLtags. | | | Itisdefinedas: | | | | | | | 1 | N | | π (q,P ,T) θold k | | | | (cid:88) | | | | foreachtrajectoryτ | | do | | | | | --- | ------ | ----- | ----------- | --- | --- | --- | ------------------ | --- | --- | --- | --- | --- | | | reward | =1.0− | penalty(v), | | (8) | 9: | | | i | | | | format | | | | | | | 10: | | Computereward:r | ϕ | (q,τ i ) | | | | --- | --- | --- | --- | --- | --- | --- | --- | --------------- | --- | -------- | --- | --- | v∈V | | | | | | | 11: | | ComputenormalizedadvantageA | | | i | | | --- | --- | --- | --- | --- | --- | --- | --- | --------------------------- | --- | --- | --- | --- | whereV denotesthesetofformatviolationsidentifiedinthe 12: Computeimportanceweightsr (θ) i,t | | | | | | | 13: | endfor | | | | | | | ---------------- | --- | ----------------------------------- | --- | --- | --- | --- | -------------- | --- | --- | ---- | --- | --- | | model’sresponse. | | Thepenaltyfunctionpenalty(v)assigns | | | | | | | | | | | | | | | | | | 14: | Updatepolicy:π | | ←π | −∇ L | | | apredefinedcosttoeachviolation,asspecifiedinTable2. θ θ θ GRPO endfor 15: | Withtheproposedrewarddesign,weoptimizethepolicy | | | | | | 16: | endfor | | | | | | | ----------------------------------------------- | --- | --- | --- | ----------- | --- | ---------- | ------ | --- | --- | --- | --- | --- | | usingtheRLobjectivedefinedinEquation3. | | | | Thecomplete | | 17: endfor | | | | | | | trainingprocedureforICRLisoutlinedinAlgorithm1. | | | | | | | convergenceduringRLtraining. | | | Forimprovedtrainingeffi- | | | | | --- | --- | --- | --- | --- | --- | ---------------------------- | --- | --- | ------------------------ | --- | --- | --- | 3.Experiment ciency,allmodelsareloadedusingbfloat16precision. 3.1.Setup WeevaluatetheeffectivenessofICRLbycom- Baselines. Backbone Models. We apply ICRL to the Qwen2.5 paringitagainstseveralstate-of-the-artmethodsfortraining model family (Yang et al., 2024a), focusing primarily on tool-augmentedLLMs. Thesebaselinesfallintothreemain Qwen2.5-3B-InstructandQwen2.5-7B-Instruct,andfurther categories. Directpromptingmethodsincludemodelsthat evaluatingonQwen2.5-14B-Instruct. Wealsoextendour performinferenceusingdirectinputsorpromptingstrate- experimentstotheQwen3series(Yangetal.,2025),particu- giessuchasChain-of-Thought(CoT)reasoning(Weietal., larlyQwen3-8B,whichincorporatesRLenhancements. All 2022). Retrieval-basedmethodsleverageexternalinfor- theseinstruction-tunedmodelsarewidelyadoptedforques- mationthroughtechniqueslikeRetrieval-AugmentedGen- tionansweringandreasoningtasks. Wechoosetheinstruct eration(RAG),includingstandardRAG(Lewisetal.,2020), variants over base models due to their strong instruction- InterleavingRetrievalChain-of-Thought(IRCoT)(Trivedi followingcapabilities,whichenablefasterandmorestable etal.,2023),andSearch-o1(Lietal.,2025a). Fine-tuning- 4 In-ContextReinforcementLearningforToolUseinLargeLanguageModels Table3.MainResultsofICRL:ExactMatch(EM)Accuracy(%)onvariousdifficultQAdatasets.Thebestperformanceissetbold.The secondbestperformanceisunderlined. DifficultQuestionAnswering | | | Model | Method | | | | | | | | Average | | | --- | --- | ----- | --------- | --- | -------- | -------- | ----- | ------- | --------- | ---- | ------- | --- | | | | | | | TriviaQA | HotpotQA | 2Wiki | Musique | Bamboogle | | | | | | | | Direct | | 28.8 | 14.9 | 24.4 | 2.0 | | 2.4 | 14.50 | | | | | | CoT | | 3.2 | 2.1 | 2.1 | 0.2 | | 0.0 | 1.52 | | | | | | IRCoT | | 31.2 | 16.4 | 17.1 | 6.7 | | 24.0 | 19.08 | | | | | | Search-o1 | | 47.2 | 22.1 | 21.8 | 5.4 | | 32.0 | 25.70 | | | | | | RAG | | 54.4 | 25.5 | 22.6 | 4.7 | | 8.0 | 23.04 | | | | | | SFT | | 29.2 | 18.6 | 24.8 | 4.4 | | 11.2 | 17.64 | | Qwen2.5-3B | | | | R1-instruct | | 44.9 | 20.8 | 27.5 | 6.0 | | 19.2 | 23.68 | | | --- | ---------- | --- | -------------- | --- | ---- | ---- | ---- | ---- | --- | ---- | ---------- | --- | | | | | RejectSampling | | 48.8 | 24.0 | 23.3 | 5.9 | | 21.0 | 24.60 | | | | | | Search-R1 | | 54.5 | 32.4 | 31.9 | 10.3 | | 26.4 | 31.10 | | | | | | ZeroSearch | | 57.4 | 27.4 | 30.0 | 9.8 | | 11.1 | 27.14 | | | | | | ICRL | | 72.6 | 35.4 | 39.2 | 20.0 | | 33.6 | 40.16+8.94 | | | | | | Direct | | 40.8 | 18.3 | 25.0 | 3.1 | | 12.0 | 19.84 | | | | | | CoT | | 18.5 | 9.2 | 11.1 | 2.2 | | 23.2 | 12.84 | | | | | | IRCoT | | 47.8 | 13.3 | 14.9 | 7.2 | | 22.4 | 21.12 | | | | | | Search-o1 | | 44.3 | 18.7 | 17.6 | 5.8 | | 29.6 | 23.20 | | | | | | RAG | | 58.5 | 29.9 | 23.5 | 5.8 | | 20.8 | 27.70 | | | | | | SFT | | 35.4 | 21.7 | 25.9 | 6.6 | | 11.2 | 20.16 | | | | Qwen2.5-7B | | R1-base | | 53.9 | 24.2 | 27.3 | 8.3 | | 29.6 | 28.66 | | | | | | R1-instruct | | 53.7 | 23.7 | 29.2 | 7.2 | | 29.3 | 28.62 | | | | | | RejectSampling | | 59.2 | 33.1 | 29.6 | 12.3 | | 35.5 | 33.94 | | | | | | Search-R1 | | 61.0 | 37.0 | 41.4 | 14.6 | | 36.8 | 38.16 | | | | | | ZeroSearch | | 65.2 | 34.6 | 35.2 | 18.4 | | 27.8 | 36.24 | | | | | | ParallelSearch | | 62.8 | 42.9 | 42.4 | 19.7 | | 41.1 | 41.78 | | | | | | ICRL | | 75.4 | 42.6 | 53.6 | 26.0 | | 48.0 | 49.12+7.34 | | based methods involve approaches such as SFT (Chung EvaluationBenchmarks. WeevaluateICRLandvarious et al., 2024), RL without search (R1) (Guo et al., 2025), baselinesonseveralwidely-usedQAbenchmarks,including andRejectionSampling(Ahnetal.,2024). Wealsoinclude TriviaQA(Joshietal.,2017),HotpotQA(Yangetal.,2018), recentRLmethodsthatintegratesearchcapabilities,suchas 2Wiki (Ho et al., 2020), Musique (Trivedi et al., 2022), Search-R1(Jinetal.,2025a),ZeroSearch(Sunetal.,2025), andBamboogle(Pressetal.,2023). Sinceourmodelsare O2-Searcher(Meietal.,2025),andParallelSearch(Zhao trainedontheNaturalQuestions(NQ)dataset,weexclude et al., 2025). These baselines provide a comprehensive NQfromtheevaluationtoavoiddataleakage. Thesebench- comparisontovalidatethegeneralityandadvantagesofour markscoverdiversedomainsandreasoningtypes,providing proposedICRLframework. acomprehensiveassessmentofmodelperformance.Further- more,toensureevaluationefficiency,werandomlysample | Training | Datasets. | | We use | the Natural | Questions (NQ) | | | | | | | | | -------- | --------- | --- | ------ | ----------- | -------------- | --- | -------------------------------- | --- | --- | --- | ----------------- | --- | | | | | | | | | upto500questionsfromeachdataset. | | | | Theselectedbench- | | dataset(Kwiatkowskietal.,2019)astheprimarytraining marksincludebothin-domaingeneralQAtasks(e.g.,Triv- | corpus. | The | dataset | is loaded | via FlashRAG | (Jin et | al., | | | | | | | | ------- | --- | ------- | --------- | ------------ | ------- | ---- | --- | --- | --- | --- | --- | --- | iaQA,HotpotQA)andout-of-domainmulti-hopQAtasks 2025b),whichprovidespreprocessedquestion-answerpairs (e.g.,2Wiki,Musique,andBamboogle),allowingustothor- | withgold-standardanswers. | | | | NQcontainsrealuserqueries | | | | | | | | | | ------------------------- | --- | --- | --- | ------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | oughlytestthegeneralizationandreasoningcapabilitiesof fromGoogleSearch,eachpairedwithWikipediapassages differentmethods. | that | includethe | correct | answer. | To support | ourproposed | | | | | | | | | ---- | ---------- | ------- | ------- | ---------- | ----------- | --- | --- | --- | --- | --- | --- | --- | trainingmethod,werandomlysampledthreequestionsfrom thewebandusedGPT-5.22togeneratefew-shotexamples Reward. The hyperparameter α in Equation 7 is set to formattedaccordingtotherollouttemplateshowninTable1. 0.8. Furthermore,fortheformatviolationpenaltiesinTa- Tosimulatereal-worldtool-usebehavior,weintegratethe ble2,theweightsaresetto0.5,0.2,0.15,0.1,0.1,and0.2 | SerperAPI3 | | | | | | | respectivelyfromtoptobottom. | | | | | | | ---------- | --- | --- | --- | --- | --- | --- | ---------------------------- | --- | --- | --- | --- | --- | acrossallmodelstoretrieveliveresultsfrom | theGoogleSearchengine. | | | Forfairness,eachqueryretrieves | | | | | | | | | | | ---------------------- | --- | --- | ------------------------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- | thetop3documentsrequiredforsearch-basedreasoning. ImplementationDetails. Weimplementourmethodus- | | | | | | | | ing the Volcano | Engine | Reinforcement | | Learning | (VeRL) | | --- | --- | --- | --- | --- | --- | --- | --------------- | ------ | ------------- | --- | -------- | ------ | 2https://platform.openai.com/docs/models/ | gpt-5.2 | | | | | | | framework | (Sheng | et al., | 2024). | Qwen2.5-3B-Instruct, | | | ------- | --- | --- | --- | --- | --- | --- | --------- | ------ | ------- | ------ | -------------------- | --- | 3https://serper.dev/ Qwen2.5-7B-Instruct,andQwen2.5-14B-Instructserveas | | | | | | | | the backbone | models, | trained | with | a learning | rate of 1e- | | --- | --- | --- | --- | --- | --- | --- | ------------ | ------- | ------- | ---- | ---------- | ----------- | 5 In-ContextReinforcementLearningforToolUseinLargeLanguageModels Table4.Results:ExactMatch(EM)Accuracy(%)onvariousQAdatasets.O2-Searcherappliescold-startSFTbeforeRL,whileour method(ICRL)appliesRLwithoutSFT. | | Model | | Method | | SFT | | DifficultQuestionAnswering | | | | Average | | | --- | ---------- | --- | ----------- | --- | --- | -------- | -------------------------- | ----- | ------- | --------- | ---------- | --- | | | | | | | | TriviaQA | HotpotQA | 2Wiki | Musique | Bamboogle | | | | | | | O2-Searcher | | ✓ | | | | | | | | | | | | | | | 59.7 | 38.8 | 37.4 | 16.0 | | 34.4 37.26 | | | | Qwen2.5-3B | | | | ✗ | | | | | | | | | | | | ICRL | | | 72.6 | 35.4 | 39.2 | 20.0 | | 33.6 40.16 | | Table5.Exampleofmulti-turnsearch-augmentedquestionanswering.ThemodelcorrectlyidentifiesGeorgeWashingtonasthepresident whosetthetwo-termprecedentandretrieveshisinaugurationdatethroughtwosearchqueries. Question Whendidthepresidentwhosettheprecedentofatwotermlimitenteroffice? | | GoldenAnswer | | | April30,1789 | | | | | | | | | | --- | ------------ | --- | --- | ------------ | --- | --- | --- | --- | --- | --- | --- | --- | ModelResponse Ineedtoknowwhichpresidentsetthisprecedent. Ialsoneedtoknowwhenhe enteredoffice.Letmestartbyfindingoutwhichpresidentitis. presidentwhosettwotermlimitprecedent Doc1(Title:“Twenty-secondAmendmenttotheUnitedStatesConstitution”) Untiltheamendment’sratification,thepresidenthadnotbeensubjecttotermlimits,butboth GeorgeWashingtonandThomasJefferson(thefirstandthird...Doc2(Title:“Amendment22 –TermLimitsforthePresidency”)ThedecisionforGeorgeWashington–theFirstPresident of the United States – to voluntarily step down from office after two terms, established an unofficial...Doc3(Title:“ThefirstU.S.presidentsethisowntermlimit”)Washingtonwrote... ItseemsGeorgeWashingtonsettheprecedent. NowIneedtofindoutwhenhe enteredoffice. whendidGeorgeWashingtonenterofficeaspresident Doc1(Title: “PresidencyofGeorgeWashington”)Washingtonwasinau- guratedasthefirstPresidentoftheUnitedStatesonApril30,1789... Doc2(Title: “George Washington”)Washingtonwasunanimouslyelected... Doc3(Title:“PresidentWashington’s FirstTerm(1789-1792)”)GeorgeWashingtonwasinaugurated... Based on the information, George Washington entered office on April 30, 1789. April30,1789 6. For each query, we sample 8 rollout trajectories with (EM)scoreof40.16,surpassingthebestcompetingmethod, atemperatureof1.0tocomputethegroup-relativeadvan- Search-R1 (31.10), by +8.94. The improvements are es- tage. Themaximumpromptlengthissetto5000tokensto peciallypronouncedonmulti-hopdatasetssuchas2Wiki accommodatefew-shotdemonstrations,andthemaximum (+7.3),Musique(+9.7),andBamboogle(+7.2),demonstrat- response length is capped at 2048 tokens, allowing up to ing ICRL’s strength in handling complex reasoning and | 6searchturnsperquery. | | | Toregularizethepolicy, | | | | weap- tool-usescenarios. | | | | | | | --------------------- | ------- | ---- | ---------------------- | --- | --------- | -------- | ------------------------ | --- | --- | --- | --- | --- | | ply a KL | penalty | with | a coefficient | | of 0.001. | Training | is | | | | | | Furthermore,onQwen2.5-7B,ICRLachievesanaverage conductedon4NVIDIAA100GPUs(80GBeach),using | | | | | | | | EMscoreof49.12, | | outperformingthestrongestbaseline, | | | | | --------------- | -------- | ------------------------------- | -------- | ------------- | --- | ----------- | --------------- | --- | ---------------------------------- | --------- | ----------- | ------------ | | abatchsizeof64. | | WeadoptFullyShardedDataParallel | | | | | | | | | | | | | | | | | | | ParallelSearch | | (41.78), | by +7.34. | It achieves | the best re- | | (FSDP) | training | with | gradient | checkpointing | | to optimize | | | | | | | sultsonfouroutoffivedatasets,includingTriviaQA(75.4), | memoryusage. | | Forretrieval,weuseaBM25retrieverthat | | | | | | | | | | | | ------------ | --- | ------------------------------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 2Wiki(53.6),Musique(26.0),andBamboogle(48.0).These returnsthetop-3documentsforeachsearchquery. resultsshowthatICRLscaleseffectivelywithmodelsize | | | | | | | | and | generalizes | well | across both | in-domain | and out-of- | | --- | --- | --- | --- | --- | --- | --- | --- | ----------- | ---- | ----------- | --------- | ----------- | 3.2.MainResults | | | | | | | | domainQAtasks. | | Theconsistentgainsoverbaselinesthat | | | | | --- | --- | --- | --- | --- | --- | --- | -------------- | --- | ----------------------------------- | --- | --- | --- | relyonsupervisedfine-tuningorrewardmodeling—such Table3presentsthemainresultscomparingICRLtoother baselines across five popular QA benchmarks. From the asZeroSearch,Search-R1,andRejectSampling—highlight results,wecanobservethat: theeffectivenessofourin-contextreinforcementlearning frameworkinlearningtool-usebehaviorswithoutexplicit accuracy-basedrewardsorsupervision. | ICRL | achieves | state-of-the-art | | | performance | across | | | | | | | | -------------- | -------- | ---------------- | -------- | --- | ----------- | ---------------- | --- | --- | --- | --- | --- | --- | | QA benchmarks. | | | As shown | in | Table | 3, ICRL signifi- | | | | | | | ICRL cantlyoutperformsallbaselinesonbothQwen2.5-3Band achieves better performance without SFT or la- Qwen2.5-7BmodelsacrossfivechallengingQAdatasets. beleddata. Table4highlightsakeyadvantageofICRL: On Qwen2.5-3B, ICRL achieves an average exact match it achieves superior performance without requiring any 6 In-ContextReinforcementLearningforToolUseinLargeLanguageModels (a)EMAccuracy (b)FinishPercent 100 | | | | | | | 3~2~0 | | 100 | | | | | | | | --- | --- | --- | --- | --- | --- | ----- | --- | --- | --- | --- | --- | --- | --- | --- | 3~2~1~0 | 80 | 75.4 | | | | | | | | | | | | | | | ------------- | ---- | --- | --- | --- | --- | --- | --- | ------------------- | --- | --- | --- | --- | --- | --- | | )%(ycaruccAME | | | | | | | | )%(hsiniFevitalumuC | | | | | | | 80 60 53.6 | | | | | | | 48 | | 60 | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 42.6 40 3~2~0 | | | | | 26.8 | 26 | | | 40 | | | | | | | | --- | ---- | --- | ---- | ---- | --- | ---- | --- | --- | --- | --- | --- | --- | ------- | --- | | | 20.8 | | | | | | | | | | | | 3~2~1~0 | | | 20 | | | 17.8 | | | 14.4 | | | | | | | | | 20 9 0 | | A | A | Wiki | | Que mboogle | | | | 0 | | | | | | | ------ | -------- | --- | ---- | ---- | ----------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Trivia | Q Hotpot | Q | | MuSi | | | | | | | | | | | | | | | 2 | | | | | | 1 | 2 | 3 | 4 | 5 6 | 7 | Ba NumberofTurns Figure2.ComparisonofQwen-7Btrainedforthreestages(3~2~0)vsfourstages(3~2~1~0).(a)EMAccuracyacrossfiveQAdatasets. (b)Cumulativefinishpercentvsnumberofsearchturns,aggregatedacrossalldatasets. Table6.Results:Qwen2.5-14BmodelsExactMatch(EM)Accuracy(%)onvariousQAdatasets.Thebestperformanceissetbold. DifficultQuestionAnswering | | | Model | | Method | | | | | | | | Average | | | | --- | ----------- | ----- | --- | ------ | -------- | ---- | -------- | ----- | ------- | --------- | ---- | ------- | --- | --- | | | | | | | TriviaQA | | HotpotQA | 2Wiki | Musique | Bamboogle | | | | | | | | | | Direct | | 52.0 | 22.6 | 28.2 | 6.0 | | 15.2 | 24.80 | | | | | Qwen2.5-14B | | | CoT | | 56.4 | 24.6 | 25.8 | 9.0 | | 40.0 | 31.16 | | | | | | | | ICRL | | 75.0 | 43.2 | 61.8 | 25.6 | | 53.6 | 51.84 | | | SFT,incontrasttoO2-Searcher,whichappliesacold-start throughin-contextreinforcementlearning. | SFT phase | before | reinforcement | | learning. | | Despite | using | | | | | | | | | ---------- | ------ | ------------- | ---------------- | --------- | ------------ | ------- | ----- | --- | --- | --- | --- | --- | --- | --- | | no labeled | tool | traces | or task-specific | | supervision, | | ICRL | | | | | | | | 4.FurtherAnalysis achievesahigheraverageEMscoreof40.16comparedto 37.26 from O2-Searcher. It outperforms O2-Searcher on 4.1.Ablationanalysis fouroutoffivedatasets,includingsubstantialgainsonTrivi- | | | | | | | | | Ablation | on curriculum | | design | for | rollout reduction. | | | --- | --- | --- | --- | --- | --- | --- | --- | -------- | ------------- | --- | ------ | --- | ------------------ | --- | aQA(+12.9)andMusique(+4.0).Theseresultsdemonstrate Weconductanablationstudycomparingtwocurriculafor thatICRLcanlearneffectivetool-usestrategiespurelyfrom reducingthenumberofexamplesusedintherolloutprocess: in-contextexamplesandreinforcementsignals,offeringa athree-stageschedule(3~2~0)andafour-stageschedule scalableanddata-efficientalternativetomethodsthatrely | | | | | | | | | (3~2~1~0). | AsshowninFigure2(a),thethree-stagevariant | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | ---------- | ----------------------------------------- | --- | --- | --- | --- | --- | oncostlyannotationandpretraining. achievessubstantiallyhigherEMaccuracyacrossallfive | | | | | | | | | QA datasets. | For | instance, | on | TriviaQA | and 2Wiki, | the | | --- | --- | --- | --- | --- | --- | --- | --- | ------------ | --- | --------- | --- | -------- | ---------- | --- | 3.3.ConcreteExamples three-stagemodelreaches75.4and53.6,comparedto20.8 Table5presentsacompletereasoningexamplefromICRL- and26.8withthefour-stageversion. | Qwen2.5-7B | on | a question | | from the | Bamboogle | dataset. | | | | | | | | | | ---------- | --- | ---------- | --- | -------- | --------- | -------- | --- | --- | --- | --- | --- | --- | --- | --- | Figure2(b)showsthatthefour-stagecurriculumleadsto Themodelistaskedwithansweringacompositionalquery: fasterdecisions,withover80%ofqueriesfinishingwithin | identifying | the | president | who | established | | the two-term | | | | | | | | | | ----------- | --- | --------- | --- | ----------- | --- | ------------ | --- | --------------- | ---------------------------------- | --- | --- | --- | --- | --- | | | | | | | | | | twosearchturns. | However,thiscomesatthecostofanswer | | | | | | precedentanddeterminingwhenheenteredoffice. Itfirst quality. These results suggest that aggressively reducing issuesasearchquerytoidentifytherelevantfigure,correctly | | | | | | | | | rollout length | too | early | (via the | intermediate | stage | with | | ---------- | ---- | ------ | ---------- | --- | ------- | ---------- | --- | -------------- | --- | ----- | -------- | ------------ | ----- | ---- | | concluding | that | George | Washington | | set the | precedent. | It | | | | | | | | oneexample)encouragesprematurestoppingandweakens thenformulatesafollow-upquerytoretrievehisinaugura- | | | | | | | | | multi-turnreasoning. | | Incontrast,thesimpler3~2~0curricu- | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | -------------------- | --- | ---------------------------------- | --- | --- | --- | --- | tiondateandsuccessfullyextractsthecorrectanswer,April lummaintainsstrongerperformancebyallowingthemodel 30,1789. ThiscaseillustratesICRL’sabilitytodecompose toexplorelongerreasoningpathsduringtraining. complexquestions,retrieverelevantinformationacrossmul- tipleturns,andmaintaincoherentreasoningwithoutexplicit intermediatesupervision. Itdemonstratestheeffectiveness ModelScalingand14BPerformance. Toevaluatethe ofourframeworkinlearningstructuredtool-usebehaviors scalabilityofICRLacrosslargermodels, weappliedour 7 In-ContextReinforcementLearningforToolUseinLargeLanguageModels | | (a)ResponseLength | | | (b)Reward | | (c)NumberofValidSearch | | | | | --- | ----------------- | --- | --- | --------- | --- | ---------------------- | --- | --- | --- | Figure3.Trainingdynamicscomparisonacrossdifferentfew-shotsettings(3-shot,2-shot,0-shot)forQwen-7Bmodel. Table7.Results:Accuracy(%)onMathQAdatasets. methodtoQwen2.5-14B-InstructandreportresultsinTa- ble6.ICRLsignificantlyoutperformsbothdirectprompting MathQA andCoTmethods acrossallfiveQAdatasets. Inparticu- Model Method SFT | | | | | | | | AIME2024 | AIME2025 | | | --- | --- | --- | --- | --- | --- | --- | -------- | -------- | --- | lar, itachieves75.0EMonTriviaQAand61.8on2Wiki, | | | | | | ReTool | ✓ | 67.0 | | 49.3 | | ----------------------------------------------- | ------------------- | --------- | --------------- | -------- | ------ | --- | ---- | --- | ---- | | yieldingastrongaverageEMscoreof51.84—surpassing | | | | Qwen3-8B | | | | | | | | | | | | ICRL | ✗ | 64.1 | | 51.7 | | CoT | by +20.7 and direct | prompting | by +27.0. These | re- | | | | | | sultsdemonstratethatICRLcontinuestoscaleeffectively tolargermodelsizesandbenefitsfromincreasedcapacity, compareourresultswithReTool,whichisaSFT-RLtrain- withoutrequiringadditionalsupervisionorannotation. ingframeworkthattrainingmodelstolearncode-writing | | | | | and tool-calling | on running | code. | ReTool | (Feng | et al., | | --- | --- | --- | --- | ---------------- | ---------- | ----- | ------ | ----- | ------- | 4.2.Trainingprocess | | | | | 2025) achieves | the state-of-the-art | | performance | on | code- | | --- | --- | --- | --- | -------------- | -------------------- | --- | ----------- | --- | ----- | TounderstandhowICRLevolvesduringtraining,weana- augmentedlong-formreasoningforsolvingmathproblems, lyzethelearningcurvesacrossthe3-shot,2-shot,and0-shot butitalsoneedsalotofannotateddatatoapplycold-start curriculumstagesusingtheQwen2.5-7Bmodel.Intheearly SFTformodelstolearnthetool-callingformatandunder- stageswithdemonstrations(3-shotand2-shot),themodel standthewholereasoningprocess. However,ourmethod producesrelativelystableandwell-structuredresponses,as ICRLdoesn’tneedtoSFTthemodelfirst,andmodelscan | reflected | in the consistent | response | lengths. As training | | | | | | | | --------- | ----------------- | -------- | -------------------- | --- | --- | --- | --- | --- | --- | stillreasoningproperlyfromtheIn-ContextLearningwith progressesintothe0-shotstage,theresponselengthinitially the examples we provides in prompt. From Table 7, al- dropsduetotheremovalofin-contextexamplesbutgradu- throughoutmethodunderperformsReToolonAIME2024 allyincreasesagain,indicatingthatthemodelislearningto by 2.9%, it can achieve better result on AIME2025 with independentlycomposelongerandmorestructuredoutputs. | | | | | +2.4%accuracy. | Whichmeansourmethodstillworksfor | | | | | | --- | --- | --- | --- | -------------- | -------------------------------- | ------------------ | ---------- | --- | ------ | | | | | | help models | to learn | other tool-calling | operations | | and is | Althoughtherewardremainsrelativelysteadythroughout moredata-efficientthanothermethodsthatneedcold-start trainingandisbasedonlyonsparsesignals—outputformat validityandfinalansweraccuracy—themodelstilllearns SFTwiththousandsofannotateddata. | tousetoolsmoreeffectivelyovertime. | | | Thisismostclearly | | | | | | | | ---------------------------------------------------- | ------------------------------------ | --- | ----------------- | ------------ | --- | --- | --- | --- | --- | | reflectedintheincreasingnumberofvalidtoolcallsduring | | | | 5.Conclusion | | | | | | | the0-shotphase. | Theriseinvalidtoolusageindicatesthat | | | | | | | | | WeintroduceICRL,asimpleyetpowerfulframeworkfor ICRLsuccessfullyencouragesthemodeltointernalizetool- usebehavior,evenwithoutdenseorstep-levelsupervision. training LLMs to use tools via in-context reinforcement | | | | | learning,withoutrequiringSFTorlabeledtooltraces. | | | | | By | | --- | --- | --- | --- | ------------------------------------------------ | --- | --- | --- | --- | --- | incorporatingfew-shotdemonstrationsdirectlyintotheRL 4.3.Generalize rolloutpromptsandgraduallyphasingthemout,ICRLen- Apart from conducting our method in web search tool- ablesmodelstotransitionfromimitationtoautonomoustool callingdomain,wealsoevaluateourmethodbytrainingthe | | | | | usethroughreward-drivenlearning. | | | Ourmethodachieves | | | | --- | --- | --- | --- | -------------------------------- | --- | --- | ----------------- | --- | --- | models’abilitiesoncode-writingandcallingtoolstorunthe strong performance across a range of QA and reasoning pythoncodetohelpthemsolvecomplexmathproblems.We benchmarks, outperforming existing approaches that rely 8 In-ContextReinforcementLearningforToolUseinLargeLanguageModels onsuperviseddataorfrozentool-usepolicies. 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