| 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 | <zhaoyiran0924@gmail.com>, | | | | Michael | Qizhe Shieh | | | | | | | | | |
| | -------- | -------------------------- | --- | --- | --- | ------- | ----------- | --- | --- | --- | --- | --- | --- | --- | --- | |
| <michaelshieh@comp.nus.edu.sg>. 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 | <t ,q,H t | ) (1) | |
| | --- | --- | --- | --- | --- | ------------- | --- | ----------- | --------- | ----- | |
| methodssuchasO2-Searcher(Meietal.,2025)thatrequire |
| t=1 |
| | cold-start SFT | to learn complex | tool-use | behavior, ICRL | | | | | | | |
| | -------------- | ---------------- | -------- | -------------- | --- | --- | --- | --- | --- | --- | |
| learnssuchcapabilitiesdirectlythroughin-contextexamples Here,π θ isthemodelparameterizedbyθ,andH t denotes |
| | | | | | the sequence | of previous | actions | taken | by the | model and | |
| | --- | --- | --- | --- | ------------ | ----------- | ------- | ----- | ------ | --------- | |
| duringRLrollouts—demonstratingsuperiordataefficiency. |
| thecorrespondingobservationsreturnedbythetoolupto |
| BeyondwebQA,wealsoevaluateICRLonmathreasoning |
| | | | | | step t. | Specifically, | the interaction | | between | the model | |
| | ----------------------------------- | ----------- | ------------- | ---------- | ------- | ------------------ | --------------- | ---------- | ----------- | --------- | |
| | tasksinvolvingcodeexecutionasatool. | | OntheAIME2024 | | | | | | | | |
| | | | | | and the | tool is structured | as | a sequence | of actions. | At | |
| | and AIME2025 | benchmarks, | ICRL matches | or exceeds | | | | | | | |
| the performance of ReTool (Feng et al., 2025), a strong each time step, the model may choose to (i) perform in- |
| ternalreasoning,(ii)issueaquerytotheexternaltool,or |
| SFT+RLbaseline,despiteusingnosupervisedpretraining. |
| | | | | | (iii) return | a final | answer. | These actions | are | embedded | |
| | --- | --- | --- | --- | ------------ | ------- | ------- | ------------- | --- | -------- | |
| 2 |
| |
| In-ContextReinforcementLearningforToolUseinLargeLanguageModels |
| | inthegeneratedtextinastructuredformat,suchasXML | | | | | | | where | | | | | | | |
| | ----------------------------------------------- | ----- | ----------- | --------- | ----- | ---- | ------------ | ---------------------- | --- | --- | ----------------- | --- | --- | --- | |
| | tags, | which | distinguish | reasoning | steps | from | tool invoca- | | | | | | | | |
| | | | | | | | | R(τi)−mean({R(τi)|τi∼π | | | (τ),i=1,2,...,N}) | | | | |
| θold |
| tions and answers. For example, a reasoning step might Ai= , (4) |
| | | | | | | | | | std({R(τi)|τi∼π | | θold (τ),i=1,2,...,N}) | | | | |
| | --- | ------- | ---------------------- | --- | --- | -------- | ----- | --- | --------------- | --- | ---------------------- | --- | --- | --- | |
| | be | denoted | as <think>...</think>, | | | a search | query | | | | | | | | |
| as <search>...</search>, a retrieved information andr (θ)=π (τ |q,τ )/π (τ |q,τ ). |
| | | | | | | | | i,t | | θ i,t | i,<t θold | i,t i,<t | | | |
| | --- | --- | --- | --- | --- | --- | --- | --- | --- | ----- | --------- | -------- | --- | --- | |
| as<information>...</information>,andafinal |
| | answeras<answer>...</answer>. | | | | | | | 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 <t | t | | |
| t=1 |
| | whereπ | | isthepolicyLLM,π | | isthereferenceLLM,r | | | | | | | | | | |
| | ------ | --- | ---------------- | --- | ------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| | | θ | | | ref | | ϕ | | | | | | | | |
| istherewardfunctionandD isKL-divergencemeasure. whereP N representsthefew-shotpromptconsistingofN |
| KL |
| | | | | | | | | demonstrationexamples. | | | Table1showsaconcreteexample | | | | |
| | --- | --- | --- | --- | --- | --- | --- | ---------------------- | --- | --- | --------------------------- | --- | --- | --- | |
| ofrollouttemplate. |
| | LossMasking. | | UnliketraditionalRL,whichoptimizes | | | | | | | | | | | | |
| | ------------ | --- | ---------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| solely over model-generated tokens, tool-augmented rea- Aftertrainingforseveralsteps,themodelbeginstoacquire |
| soning introduces retrieved content into the rollout se- tool-usecapabilitieswiththeguidanceoftheinitialfew-shot |
| quence—tokens that are not produced by the model and promptP . Oncesufficientlearningprogressisobserved, |
| N |
| therefore do not reflect its internal reasoning or decision- wepausetrainingandreducethenumberofdemonstration |
| | makingprocess. | | Toaddressthis,weadoptalossmasking | | | | | | | | | | | | |
| | -------------- | --- | --------------------------------- | --- | --- | --- | --- | -------------------- | --- | --- | ----------------------------- | --- | --- | --- | |
| | | | | | | | | examplesintheprompt. | | | Theupdatedpolicyconditionedon | | | | |
| strategytailoredforRLwithtooluse, whichexcludesre- areducedpromptP isdefinedas: |
| N−1 |
| | trieved | content | from | the optimization. | | Specifically, | only | | | | | | | | |
| | ------------------------------------------------ | ------- | ---- | ----------------- | --- | ------------- | ---- | --- | --- | --- | --- | --- | --- | --- | |
| | tokensgeneratedbythelanguagemodelcontributetothe | | | | | | | | | | |y| | | | | |
| (cid:89) |
| | | | | | | | | π (y |P | | ,q,T)= | π (y |P | ,y | ,q,H | ), (6) | |
| | --- | --- | --- | --- | --- | --- | --- | ------- | --- | ------ | ------- | --- | ---- | ------ | |
| policygradient,whileretrievedspansaremaskedoutand θ N−1 θ t N−1 <t t |
| | excludedfromthelosscomputation. | | | | Thistargetedoptimiza- | | | | | | t=1 | | | | |
| | ------------------------------- | --- | --- | --- | --------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| tionensuresthatlearningremainsfocusedonthemodel’s whereP denotesapromptwithN −1demonstration |
| N−1 |
| ownbehavior—suchastoolusage,intermediatereasoning, |
| | | | | | | | | examples. | Thisprocessisrepeatediteratively,progressively | | | | | | |
| | --- | --- | --- | --- | --- | --- | --- | --------- | ---------------------------------------------- | --- | --- | --- | --- | --- | |
| andfinalanswers—withoutbeingaffectedbyfixed,untrain- |
| reducingthenumberofdemonstrations,untilnoexamples |
| | ablecontentfromexternalsources. | | | | | | | remainintheprompt. | | | | | | | |
| | ------------------------------- | --- | --- | --- | --- | --- | --- | ------------------ | --- | --- | --- | --- | --- | --- | |
| GRPOwithToolUse. WeadoptGRPO(Shaoetal.,2024) RewardDesign. Wedesignacompositerewardfunction |
| | totrainπ | | ontheRLdatasetD | | ={q ,q | ,··· ,q | }. Specif- | | | | | | | | |
| | -------- | --- | --------------- | --- | ------ | ------- | ---------- | ------------------------------------------------- | --- | --- | --- | --- | --- | --- | |
| | | θ | | | 1 | 2 | n | thatcombinestheansweraccuracyandformatcorrectness | | | | | | | |
| ically,forq ∈D,weusetheoldpolicyfrompreviousstep toprovidearicherlearningsignal: |
| | π | tosampleagroupofN | | individualresponsesτ | | | . Then, | | | | | | | | |
| | --------------------- | ----------------- | --- | -------------------- | --- | -------- | -------- | ---------------- | ------ | -------------- | ------------- | ------- | ------------ | ----- | |
| | θold | | | | | | i | | | | | | | | |
| | theRLlossisdefinedas: | | | | | | | r (q,y)=α·reward | | | +(1−α)·reward | | | , (7) | |
| | | | | | | | | ϕ | | | acc | | format | | |
| | | | | | | | |τi| | where α | is the | hyperparameter | to | balance | two rewards. | | |
| | | | | | 1 | N | | | | | | | | | |
| | | (θ)=E | | | | (cid:88) | (cid:88) | | | | | | | | |
| L GRPO Specifically, the accuracy-based reward is computed us- |
| | | | τi∼πθold | (q),q∼DRL(cid:80)N | | |τ | | (3) | | | | | | | | |
| | --- | --- | -------- | ------------------ | --- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | |
| i=1 i i=1 t=1 ing exact match (EM) between the model’s predicted an- |
| | | | | | ,ϵ)−β·D | | | swer and | the | ground | truth. The reward | is | assigned | as | |
| | --- | --- | ------ | --------- | ------- | ----- | ----------- | -------- | --- | ------ | ----------------- | --- | -------- | --- | |
| | | | CLIP(r | i,t (θ),A | i | KL [π | θ ∥π ref ], | | | | | | | | |
| 3 |
| |
| In-ContextReinforcementLearningforToolUseinLargeLanguageModels |
| Table1.Few-shotrollouttemplateinICRL. |
| Few-ShotPromptTemplate |
| Solvethefollowingproblemstepbystep. Youmustconductreasoninginside<think>...</think>everytimeyougetnew |
| information.Afterreasoning,ifyoufindyoulacksomeknowledge,youcancallasearchengineby<search>query</search> |
| anditwillreturnresultsbetween<information>...</information>.Youcansearchasmanytimesasyouwant.Finally, |
| providetheanswerinside<answer>...</answer>. |
| Herearesomeexamples: |
| ExampleProblem:q |
| demo |
| ExampleSolution:<think>...</think><search>...</search> |
| <information>...</information><think>...</think><answer>a</answer> |
| | (repeatedforN | | examples | | | | | | | | | | | |
| | ------------- | --- | -------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| Nowsolvethefollowingproblem: |
| ActualProblem:question |
| | Table2.Formatviolationpenaltiesforcomputingreward | | | | | . | | | | | | | | |
| | ------------------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| format |
| Algorithm1ICRL |
| | Violation | | | Rationale | | | | | | | | | | |
| | --------- | --- | --- | --------- | --- | --- | ------ | ------- | -------- | ----------- | ----- | --- | ------ | |
| | | | | | | | Input: | Initial | policy π | , reference | model | π | , tool | |
| | | | | | | | | | θ | | | ref | | |
| No<answer>tag Mustprovidestructuredanswer T, initial few-shot prompt P , dataset partitions |
| N |
| Unbalanced<answer>tags ProperXMLstructurerequired {D(N),D(N−1),...,D(0)}, reward function r (·), |
| ϕ |
| | No<think>tag | | | Shoulddemonstratereasoning | | | | | | | | | | |
| | --------------------- | --- | --- | -------------------------- | --- | --- | ---------------- | --- | --- | --- | --- | --- | --- | |
| | Unbalanced<think>tags | | | | | | numberofRLstepsT | | | | | | | |
| ProperXMLstructurerequired |
| No<search>usage 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 <think>Ineedtoknowwhichpresidentsetthisprecedent. Ialsoneedtoknowwhenhe |
| enteredoffice.Letmestartbyfindingoutwhichpresidentitis.</think> |
| <search>presidentwhosettwotermlimitprecedent</search> |
| <information>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... |
| </information> |
| <think>ItseemsGeorgeWashingtonsettheprecedent. NowIneedtofindoutwhenhe |
| enteredoffice.</think> |
| <search>whendidGeorgeWashingtonenterofficeaspresident</search> |
| <information>Doc1(Title: “PresidencyofGeorgeWashington”)Washingtonwasinau- |
| guratedasthefirstPresidentoftheUnitedStatesonApril30,1789... Doc2(Title: “George |
| Washington”)Washingtonwasunanimouslyelected... Doc3(Title:“PresidentWashington’s |
| FirstTerm(1789-1792)”)GeorgeWashingtonwasinaugurated...</information> |
| <think> Based on the information, George Washington entered office on April 30, 1789. |
| </think> |
| <answer>April30,1789</answer> |
| 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. ICRLalso Hsiao, V., Fine-Morris, M., Roberts, M., Smith, L. N., |
| generalizesacrossdomains,includingwebsearchandcode and Hiatt, L. M. A critical assessment of LLMs for |
| execution, demonstrating its flexibility and effectiveness. solving multi-step problems: Preliminary results. In |
| TheseresultshighlightICRLasascalableanddata-efficient AAAI 2025 Workshop LM4Plan, 2025. URL https: |
| alternative to traditional SFT+RL pipelines for enabling //openreview.net/forum?id=kFrqoVtMIy. |
| tool-augmentedlanguagemodels. |
| | | | | | Jin, B., Zeng, | H., | Yue, Z., | Yoon, | J., | Arik, S., | Wang, D., | |
| | --- | --- | --- | --- | -------------- | ------- | -------- | ------------- | --- | --------- | --------- | |
| | | | | | Zamani, | H., and | Han, | J. Search-r1: | | Training | llms to | |
| ImpactStatements |
| reasonandleveragesearchengineswithreinforcement |
| Thispaperpresentsworkwhosegoalistoadvancethefield learning. arXivpreprintarXiv:2503.09516,2025a. |
| | of machine | learning. There | are many potential | societal | | | | | | | | |
| | ---------- | --------------- | ------------------ | -------- | --- | --- | --- | --- | --- | --- | --- | |
| consequencesofourwork,noneofwhichwefeelmustbe Jin, J., Zhu, Y., Dou, Z., Dong, G., Yang, X., Zhang, C., |
| | | | | | Zhao, T., | Yang, | Z., and | Wen, | J.-R. | Flashrag: | A mod- | |
| | --- | --- | --- | --- | --------- | ----- | ------- | ---- | ----- | --------- | ------ | |
| specificallyhighlightedhere. |
| ulartoolkitforefficientretrieval-augmentedgeneration |
| | | | | | research. | InCompanionProceedingsoftheACMonWeb | | | | | | |
| | --- | --- | --- | --- | --------- | ----------------------------------- | --- | --- | --- | --- | --- | |
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