Ci bi mu melGis bi ci topp
Agent Planner-Executeur
Aplikaasioŋ yi
GUIDE ci aplikaasioŋ yi
Modèlu Agent ReAct
ReAct is a language-model prompting pattern that interleaves reasoning traces with actions and observations from an external tool or environment.
Déglul tegtal biiIA-nettali · ~1 min
Ci xët wii1 simili jàng
Résumé
The original paper evaluates it on knowledge-intensive question answering and interactive decision-making tasks.
Plongeur bu xóot
The ReAct paper prompts language models to interleave reasoning traces and task-specific actions, using observations from an external environment to update plans. It evaluates HotpotQA and FEVER with a Wikipedia API and interactive decision benchmarks including ALFWorld and WebShop. The authors report a 34-point absolute success-rate improvement on ALFWorld and 10 points on WebShop over the baselines used in their experiments. These findings are specific to the tested tasks and prompts.
Doxal ci àdduna dëgg
Representing a trajectory as Thought, Action, Observation and a final answer.
Using the paper’s Wikipedia API to retrieve evidence for HotpotQA and FEVER questions.
Updating a plan after an environment returns an observation.
Comparing the original paper’s ReAct results with reasoning-only and acting-only baselines in its tested tasks.
Ëlëgu motif agent ReAct
Evaluate ReAct-style loops on the specific environment, tool reliability and task distribution. Include access limits, error handling and stopping conditions; the paper’s benchmark results do not guarantee safety or accuracy in unrelated deployments.
Teg ko ci jëf
Lu tax mu am solo
- Tabax tànneef. Ni ñuy jëmmale aplikaasioŋ bi mooy wane ndax IA dafay gëna baaxal njariñ yi.
- Ekip ak def liggéey. Integraasioŋ bu baax ci def liggéey dafay jur njariñu liggéey bu jëfandikukat yi mëna wóolu.
- Risk ak kaaraange. Jëfandikoo bu jaar yoon dina wàññi coono coppite ak risku samp gi.
Moytul
- Otomatise procédure bu yàqu mën na yokk jafe-jafe yi fi nekk.
- Ekip yi mën nañu otomatise lu ëpp ba noppi dindi àtteb nit ñi.
- Kalite mën na wàññeeku sudee duñu wéy di jàngat li ñuy génne.
Jéego yi ci topp
- Defal kàrt ni liggéey bi di doxee leegi nga ràññee jéego bi gëna am jafe-jafe.
- Mandargal barabu saytu nit balaa otomatisasioŋ bu mat sëkk.
- Taggat jëfandikukat yi ci ay laaj, yooni eskalaasioŋ ak seeni sàrti kalite.
- Toppal njariñu niveau liggéey bi ngir firndeel valeur buy wéy.
Weyal di banneexu
Bulletin bu amul fayda
Jàngal xibaar AI bu bes
Ñatti jaar-jaari IA yuñ firndeel bis bu nekk ci ayu-bis bi, ñu bind leen ci Àngle bu leer. Gratis ba fàww, amul yëgle.
Benn email bis bu nekk ci ayu-bis bi. Benn klik nga dindi abonemaa bi. Dunu musa jaay wala séddoo sa adres.
Nattal sa bopp
Take the ReAct Agent Pattern quiz
Feedback ci saasi ci bépp tontu, ak sertifikaa buñ mëna séddoo ak ID buñ mëna saytu soo jàllee ci njàng mi.
Jàppale njàngum IA te doo fay. AI Understanding mbootaay la buy def te Yàlla tax — amul yëgle, amul paywall, musul am. Defal ab sarax
Laaj yi ñuy faral di laaj
Luy motif agent ReAct?
ReAct is a language-model prompting pattern that interleaves reasoning traces with actions and observations from an external tool or environment. The original paper evaluates it on knowledge-intensive question answering and interactive decision-making tasks.
Yan ñaari mënin la motif ReAct boole?
ReAct mooy Xalaat ak Jëf; dafay boole xalaat yu bari ak jëf yu fëgër yu melni woote jumtukaay.
Lan mooy ñetti mbir yiy baamtu ci ReAct loop?
Bouclage ReAct dafay jaar ci Xalaat (plan), Jëf (wootu jumtukaay), ak Seetlu (resultaa bi), di baamtu bañu jeex.
Luy 'Seetlu' ci loop ReAct?
Seetlu bi mooy feedback bi sistem bi di delloo ginaaw bimu defee Jëf ji model bi tànn, model bi daal di koy xalaat.
Lan moo waral ReAct mëna wàññi gis-gis bu jaarul yoon buñu ko méngale ak xalaat bu jaar yoon?
Suñu boolee xalaat ak seetlu yu dëggu yi bawoo ci jumtukaay, model bi mën na xool dëgg yi ak jubbanti yoon wi moo gën sos ay tontu.
What improvement did the ReAct paper report over imitation/RL baselines on ALFWorld?
The original ReAct paper reports an absolute success-rate improvement of 34 points over imitation and reinforcement-learning baselines on ALFWorld, with a separate 10-point result on WebShop; this is scoped to its experiments.
Weyal di jàng
Gid yu jëm ci loolu
Tann nañu yeneen njiit ngir topic bii