CMU Researchers Introduce PPP and UserVille To Prepare Proactive And Personalised LLM Brokers

Most LLM brokers are tuned to maximise job success. They resolve GitHub points or reply deep analysis queries, however they don’t purpose fastidiously about when to ask the consumer questions or find out how to respect completely 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 workforce of researchers from Carnegie Mellon College CMU and OpenHands formalizes these lacking behaviors as 3 joint goals, Productiveness, Proactivity, and Personalization, and optimizes them with a multi goal reinforcement studying framework referred to as PPP inside a brand new atmosphere named UserVille.

From job success to interplay conscious brokers

The analysis workforce defines:

• Productiveness as job completion high quality, for instance F1 on SWE-Bench Verified perform localization or precise 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 equivalent to brevity, format, or language.

UserVille, an interactive atmosphere with desire conscious simulators

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

It has 3 phases:

1. Immediate Vaguenization: Exact job prompts are rewritten into obscure prompts that maintain the identical intent however take away particulars. This creates data 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 desire from a pool of 20 varieties. Preferences cowl brevity, variety of questions per flip, reply format, timing, language constraints, or necessities equivalent to JSON formatted questions. Twelve preferences are utilized in coaching and eight preferences are held out for generalization exams.
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 may possibly reply utilizing the exact immediate and the way laborious 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 desire, in any other case 0, averaged over periods the place the agent requested no less than 1 query.

UserVille is instantiated on 2 domains, software program engineering with SWE-Gymnasium 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’ll name area instruments and an ask_user software that queries the consumer simulator.

PPP defines a trajectory stage reward

R = R_Prod​ + R_Proact​ + R_Pers​.

• Productiveness reward R_Prod​ is the duty metric, F1 on SWE-Func-Loc or precise 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 desire and provides non optimistic penalties outlined by the desire particular rule for every violation.

Coaching makes use of a GRPO primarily based RL algorithm with the Clip Greater technique and token stage 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 achieve 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 sequence 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 underneath obscure prompts improves by 21.66 factors.

PPP additionally adjustments interplay conduct. The ask ratio on SWE-Func-Loc rises from 50 % to 100% underneath obscure prompts and from 51 % to 85 % 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 downside that collectively optimizes Productiveness, Proactivity, and Personalization, as an alternative of focusing solely on job success.
2. UserVille builds obscure immediate variations of current benchmarks and pairs them with desire conscious consumer simulators, which implement 20 distinct interplay preferences and label consumer effort ranges.
3. The entire reward combines job metric, consumer effort, and desire adherence, utilizing bonuses for low effort questions and penalties for medium and excessive effort or desire 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 achieve of about 16.72 factors throughout dimensions and datasets.
5. PPP brokers generalize to unseen preferences, alternate simulators, and more durable duties equivalent 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 desire conscious consumer simulators that implement 20 interplay preferences, and apply GRPO with DAPO model token stage 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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