arXiv:2606. 15532v1 Announce Type: cross Abstract: Emotional intelligence (EI) in Large Language Models (LLMs) is often evaluated through static understanding tasks or single-response dialogue generation.
arXiv:2603. 06194v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) for large language models (LLMs) has shown strong performance in single-turn tasks, but extending it to multi-turn interaction remains challenging due to sparse rewards and poor per-turn credit assignment.
arXiv:2606. 03892v1 Announce Type: cross Abstract: Training LLMs to orchestrate multi-step tool calls is held back by three coupled obstacles: realistic stateful execution environments are costly to build, synthetic training queries are often detached from the server's actual state (so the generated tool calls fail to execute), and recall-based RL rewards incentivize verbose tool-calling patterns.
arXiv:2606. 14199v1 Announce Type: cross Abstract: Large language models are increasingly deployed as human simulators for interactive evaluation and social simulation.
arXiv:2607. 07508v1 Announce Type: cross Abstract: Reinforcement learning (RL) is becoming increasingly important for post-training large language models (LLMs).
arXiv:2606. 11119v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models.
arXiv:2608. 09168v1 Announce Type: new Abstract: Agent skills are increasingly used to equip large language model (LLM) agents with reusable procedural knowledge.
arXiv:2607. 27816v2 Announce Type: replace-cross Abstract: Role-playing agents (RPAs) have become one of the most important consumer applications of large language models.
The paper introduces FIGS, a dual‑axis evaluation framework for multi‑turn sycophancy that avoids penalizing empathy. It uses a 10‑turn conversational simulator with 500 diverse scenarios to test whether models stay truthful while keeping praise proportional, and whether they show calibrated validation of user feelings. The study finds that current models either drift toward sycophancy or become overly detached, highlighting an unresolved trade‑off in sustained dialogue.
arXiv:2607. 17191v1 Announce Type: new Abstract: Human-like private chat requires more than fluent response generation: a system must preserve persona, relationship, memory, bounded knowledge, medium-specific timing, and a coherent multi-turn arc.
Iterative GRPO is a batch‑online policy iteration framework that enables multi‑turn reinforcement learning for conversational agents without requiring an interactive user simulator. It alternates between learning a turn‑level Q‑function from logged returns (policy evaluation) and applying single‑turn GRPO against this Q‑function (policy improvement), thereby scoring candidate responses by their expected downstream return. The method is validated on six multi‑turn negotiation environments, demonstrating its practicality for real‑world deployment patterns.
The paper introduces FACA, a Feedback‑Aware Credit Assignment method that aligns each agent reaction with the preceding user‑to‑user segment, computes a locally normalized reaction advantage, and adds it to the terminal outcome advantage without requiring an extra critic or rollout. Experiments show that FACA improves performance across nine domains, especially in Telecom, and maintains the same ordering of gains in zero‑shot benchmarks such as Pare‑Bench and Co‑Gym.
The paper introduces FACA, a Feedback‑Aware Credit Assignment method that aligns each agent reaction with the preceding user‑to‑user segment, derives a locally normalized reaction advantage, and adds it to the terminal outcome advantage without requiring an extra critic or rollout. Compared to an outcome‑only Interactive GRPO baseline, FACA improves performance across nine domains by 5.91–10.22 percentage points on 8B and 14B models, with notable gains in Telecom. The approach demonstrates that next‑turn user reactions provide actionable local credit for enhancing multi‑turn user‑interacting agents.