openclaw-rl

Binary Reward Summarized from Next State

Online RL for agentic tool-use, using binary process reward signals from environment feedback.

Method Overview

The policy model is deployed as an OpenAI-compatible chat proxy. External environments (e.g. OpenClaw) send multi-turn conversations through this proxy. For each main-line turn, the system:

1. Forwards the request to the policy model (served by SGLang) and collects the response along with per-token log-probabilities.
2. When the next turn arrives, its user/environment message serves as the "next state" for the previous turn.
3. A Process Reward Model (PRM) judges the previous response quality given the next state (could be user or env feedback). It produces m independent evaluations via majority vote, scoring each turn as +1 (good), -1 (bad), or 0 (neutral).
4. The majority-voted score becomes the scalar reward for that turn.
5. Turns that never receive a next state (i.e. the last turn in a session) are excluded from training (loss_mask = 0), unless they are the only turn in the session (at-least-one guarantee).

Advantage Estimation (GRPO)

Advantages are computed using Group Relative Policy Optimization (GRPO). For each sample with scalar reward r, the advantage is broadcast uniformly to all response tokens:

$$A_t = r, \quad \forall t \in \text{response tokens}$$

No reward normalization is applied (--disable-rewards-normalization).

Policy Gradient Loss

Standard PPO-style clipped surrogate objective with asymmetric clipping:

$$\rho_t = \frac{\pi_\theta(a_t \mid s_t)}{\pi_{\text{old}}(a_t \mid s_t)}$$

$$\mathcal{L}_{\text{pg}} = -\mathbb{E}_t\Big[\min!\big(\rho_t A_t,\ \text{clip}(\rho_t,, 1-\varepsilon,, 1+\varepsilon_{\text{high}}) \cdot A_t\big)\Big]$$

where $\varepsilon = 0.2$, $\varepsilon_{\text{high}} = 0.28$.

Total Loss

$$\mathcal{L} = \mathcal{L}_{\text{pg}} + \beta_{\text{KL}} \cdot \mathcal{L}_{\text{KL}}$$

where $\beta_{\text{KL}} = 0.02$. Entropy bonus is disabled ($\beta_{\text{ent}} = 0$).

How to Run

cd slime
bash ../openclaw-rl/run_qwen3_4b_openclaw_rl.sh

File Structure

openclaw-rl/
├── README.md
├── run_qwen3_4b_openclaw_rl.sh     # Launch script
├── openclaw_api_server.py           # FastAPI proxy + PRM scoring + sample submission
├── openclaw_rollout.py              # Async rollout worker (bridges API server ↔ SLIME trainer)
└── results/                         # Runtime records (auto-created)