CMU Researchers Introduce PPP and UserVille To Practice Proactive And Customized LLM Brokers

Most LLM brokers are tuned to maximise process success. They resolve GitHub points or reply deep analysis queries, however they don’t motive fastidiously about when to ask the consumer questions or learn how to respect totally different interplay preferences. How can we design LLM brokers that know when to ask higher questions and adapt their conduct to every particular person consumer?

A staff of researchers from Carnegie Mellon College CMU and OpenHands formalizes these lacking behaviors as 3 joint aims, Productiveness, Proactivity, and Personalization, and optimizes them with a multi goal reinforcement studying framework known as PPP inside a brand new atmosphere named UserVille.

From process success to interplay conscious brokers

The analysis staff defines:

• Productiveness as process completion high quality, for instance F1 on SWE-Bench Verified operate localization or actual match on BrowseComp-Plus.
• Proactivity as asking important clarifying questions when the preliminary immediate is obscure whereas avoiding pointless queries.
• Personalization as following consumer particular interplay preferences corresponding to brevity, format, or language.

UserVille, an interactive atmosphere with choice conscious simulators

UserVille converts present agent benchmarks into an interplay centric RL atmosphere populated by LLM primarily based consumer simulators.

It has 3 levels:

1. Immediate Vaguenization: Exact process prompts are rewritten into obscure prompts that preserve the identical intent however take away particulars. This creates info asymmetry, the simulator nonetheless observes the exact immediate, the agent solely sees the obscure model.
2. Choice Conscious Consumer Simulation: Every consumer simulator is parameterized by a choice from a pool of 20 varieties. Preferences cowl brevity, variety of questions per flip, reply format, timing, language constraints, or necessities corresponding to JSON formatted questions. Twelve preferences are utilized in coaching and eight preferences are held out for generalization assessments.
3. Consumer Centric Analysis: After the duty, the simulator labels every query as low effort, medium effort, or excessive effort primarily based on whether or not it might probably reply utilizing the exact immediate and the way arduous it’s to reply. Proactivity rating is 1 if the general session is low effort, in any other case 0. Personalization rating is 1 if the agent follows the choice, in any other case 0, averaged over classes the place the agent requested at the least 1 query.

UserVille is instantiated on 2 domains, software program engineering with SWE-Health club for coaching and SWE-Bench Verified and SWE-Bench Full for analysis, and deep analysis with BrowseComp-Plus and a search plus open_page software scaffold.

PPP, multi goal RL for productive, proactive, and personalised brokers

Brokers are carried out as ReAct model software utilizing insurance policies primarily based on Seed-OSS-36B-Instruct. They will name area instruments and an ask_user software that queries the consumer simulator.

PPP defines a trajectory degree reward

R = R_Prod​ + R_Proact​ + R_Pers​.

• Productiveness reward R_Prod​ is the duty metric, F1 on SWE-Func-Loc or actual match on BrowseComp-Plus.
• Proactivity reward R_Proact provides a bonus of +0.05 if all questions within the session are low effort and applies penalties of −0.1 for every medium effort query and −0.5 for every excessive effort query.
• Personalization reward R_Pers​ provides +0.05 when the agent follows the choice and provides non constructive penalties outlined by the choice particular rule for every violation.

Coaching makes use of a GRPO primarily based RL algorithm with the Clip Larger technique and token degree coverage gradient loss from DAPO, and solely optimizes LLM generated tokens. The coaching atmosphere is carried out with Verl. Seed-OSS-36B-Instruct is educated for 200 steps with batch dimension 64 and group dimension 8. Most output lengths are 32k tokens for SWE-Func-Loc, 65k for SWE-Full, and 41k for deep analysis. GPT 5 Nano is used because the consumer simulator. SWE scaffolds are primarily based on OpenHands, and deep analysis makes use of a search software and an open_page software with Qwen3-Embed-8B as retriever.

Experimental outcomes

The table-2 (beneath picture) evaluates productiveness, proactivity, and personalization on SWE-Bench Verified Func-Loc and BrowseComp-Plus, utilizing obscure prompts and averaging over 20 preferences.

For the Seed-OSS-36B-Instruct base mannequin:

• on SWE-Func-Loc, productiveness 38.59, proactivity 43.70, personalization 69.07
• on BrowseComp-Plus, productiveness 18.20, proactivity 37.60, personalization 64.76.

After PPP RL coaching, the PPP mannequin reaches:

• on SWE-Func-Loc, productiveness 56.26, proactivity 75.55, personalization 89.26
• on BrowseComp-Plus, productiveness 26.63, proactivity 47.69, personalization 76.85.

The common acquire throughout all 3 dimensions and each datasets is 16.72 factors relative to Seed-OSS-36B-Instruct and PPP additionally outperforms GPT 5 and different GPT collection baselines on the mixed metric.

Interplay is essential for obscure prompts. On SWE-Func-Loc, F1 with exact prompts and no interplay is 64.50. With obscure prompts and no interplay it drops to 44.11. Including interplay with out RL doesn’t recuperate this hole. With PPP coaching and interplay, F1 beneath obscure prompts improves by 21.66 factors.

PPP additionally adjustments interplay conduct. The ask ratio on SWE-Func-Loc rises from 50 p.c to one hundred pc beneath obscure prompts and from 51 p.c to 85 p.c on deep analysis, whereas remaining low for exact prompts. The variety of questions per session will increase early in coaching, then stabilizes with a excessive proportion of low effort questions and only a few excessive effort questions.

Key Takeaways

1. PPP frames agent coaching as a multi goal RL drawback that collectively optimizes Productiveness, Proactivity, and Personalization, as a substitute of focusing solely on process success.
2. UserVille builds obscure immediate variations of present benchmarks and pairs them with choice conscious consumer simulators, which implement 20 distinct interplay preferences and label consumer effort ranges.
3. The overall reward combines process metric, consumer effort, and choice adherence, utilizing bonuses for low effort questions and penalties for medium and excessive effort or choice violations, carried out with a GRPO primarily based RL algorithm.
4. On SWE Bench Func Loc and BrowseComp Plus with obscure prompts, PPP educated Seed OSS 36B considerably improves all 3 metrics over the bottom mannequin and over GPT 5 baselines, with a mean acquire of about 16.72 factors throughout dimensions and datasets.
5. PPP brokers generalize to unseen preferences, alternate simulators, and more durable duties corresponding to SWE Bench Full, and so they study to ask fewer however extra focused low effort questions, particularly when prompts are obscure.

PPP and UserVille mark an necessary step towards interplay conscious LLM brokers, since they explicitly encode Productiveness, Proactivity, and Personalization within the reward design, use choice conscious consumer simulators that implement 20 interplay preferences, and apply GRPO with DAPO model token degree optimization inside Verl and OpenHands scaffolds. The enhancements on SWE Bench Func Loc, SWE Bench Full, and BrowseComp Plus present that interplay modeling is now a core functionality, not an auxiliary characteristic.

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