# Teaching a 7B Model to Be On-Call

### An OpenEnv benchmark and a four-stage GRPO pipeline that turns Qwen2.5-7B into a working SRE triage agent

---

> **TL;DR.** We built `incident_env` — an OpenEnv POMDP where an LLM agent has to diagnose a live, evolving production incident and then attribute it to a specific commit in a small repo. Then we trained **Qwen2.5-7B-Instruct** through a four-stage curriculum (baseline rollouts → LoRA SFT → online GRPO with `r_cross` → merge). The post-trained model reaches a **mean cumulative reward of ≈1.59 vs ≈0.49** for the base, **at less than half the steps**, with tighter variance and dominant CDF across the operating range.


![image](https://cdn-uploads.huggingface.co/production/uploads/66e56109975df8fffc75f3c7/bNlv5ywRBRCj3Al1BKi8R.png)

> 🧭 **One-line pitch.** *Most agent benchmarks freeze a repo and ask the model to fix it. Our environment refuses to sit still — memory climbs, alerts cascade, and the obvious symptom is almost never the cause.*

---

## 1 · Why this benchmark didn't exist yet

Pick any list of agentic LLM benchmarks today and you'll see two clusters:

| Cluster | Examples | What they miss |
| --- | --- | --- |
| **Frozen-repo coding** | SWE-bench, RepoBench, HumanEval | No evolving system, no observability, no alerts |
| **Tool-use chains** | AgentBench, ToolBench, τ-bench | Plenty of API calls, but no reactive simulator |

Neither cluster matches the workflow that consumes the most engineer-hours at any company running real systems: **on-call triage**. A pager fires. A graph is wrong. Three services look broken but only one *is* broken. Someone has to triangulate, propose a fix, and identify the offending commit — under SLA pressure, with partial information.

That gap is exactly what `incident_env` fills.

> ✦ **Capability gap.** Today's LLMs can read a static repo. They cannot yet diagnose a system whose state changes while they're looking at it.

---

## 2 · Environment at a glance

`incident_env` is an OpenEnv `Environment` — clean Gym-style `reset()` / `step()` / `state` plus a `/score` endpoint for the oracle-independent grader. Under the hood it is a **reactive, partially-observable, two-phase** simulator.

### Topology — seven reactive services

```
     ┌─────────┐    ┌─────┐    ┌────────┐    ┌─────────┐
     │ API GW  │───▶│Auth │───▶│ Orders │───▶│ Payment │
     └────┬────┘    └─────┘    └───┬────┘    └────┬────┘
          ▼                        ▼              ▼
     ┌─────────┐              ┌─────────┐    ┌─────────┐
     │  Cache  │              │   DB    │    │  Queue  │
     └─────────┘              └─────────┘    └─────────┘
```

Each service has live metric history (CPU, memory, p50/p95/p99 latency, error rate, RPS), structured logs, deploy history, and a `healthy | degraded | down` status. Faults propagate along this graph each `tick()`. Restarting a downstream service buys minutes; rolling back the wrong deploy makes things worse.

### The agent loop


![image](https://cdn-uploads.huggingface.co/production/uploads/66e56109975df8fffc75f3c7/sLaYeQmysnBDQw-VcmYsp.png)

Per-step execution is `validate → mutate → tick → observe → reward`. Two facts make the loop interesting:

1. The observation **never** exposes `fault_type`, the `is_bad` deploy flag, or any internal simulation state. The agent infers from symptoms.
2. The action space is **hierarchical and masked**. `valid_actions[]` is recomputed every step, so illegal actions (e.g. rollback on a service with no deploy history) are flagged with a `-0.05` penalty.


![image](https://cdn-uploads.huggingface.co/production/uploads/66e56109975df8fffc75f3c7/UVcgYXYA0j-1ZN8RxN3Cd.png)

---

## 3 · Two-phase action design (this is the novel bit)

Most environments give the agent one type of tool. Ours gives it two — and forces a deliberate transition between them.

```mermaid
stateDiagram-v2
    [*] --> Phase1
    state Phase1 {
        [*] --> Investigating
        Investigating --> Investigating : view_alerts / query_logs / check_metrics<br/>check_dependencies / check_deploy_history<br/>run_health_check
        Investigating --> Remediating  : restart_service / rollback_deploy / scale_service
        Remediating --> Investigating
        Investigating --> Declared     : declare_root_cause
    }
    Phase1 --> Phase2 : transition_to_phase2(belief)
    state Phase2 {
        [*] --> Exploring
        Exploring --> Exploring : list_dir / read_file / search_code<br/>get_git_log / get_file_diff
        Exploring --> Patched   : propose_patch / declare_no_change
    }
    Patched --> [*]
    Declared --> [*]
```

### Phase 1 — ops investigation

The same tools an SRE has at 3 AM, plus a `transition_to_phase2` control action that hands a structured `BeliefState` over to Phase 2:

| Action | Category | Purpose |
| --- | --- | --- |
| `view_alerts` | diagnostic | List firing alerts |
| `query_logs` | diagnostic | Filter by service/level/keyword |
| `check_metrics` | diagnostic | 30-min time series |
| `check_dependencies` | diagnostic | Up/downstream graph |
| `check_deploy_history` | diagnostic | Recent deploys |
| `run_health_check` | diagnostic | Ping a service |
| `restart_service` | remediation | Temporary fix |
| `rollback_deploy` | remediation | Real fix if root cause |
| `scale_service` | remediation | More replicas |
| `declare_root_cause` | terminal | Diagnosis string |
| `transition_to_phase2` | control | Hand off to code attribution |

### Phase 2 — code attribution

When a scenario has a `code_context`, the env spins up a sandboxed `CodeWorkspace` over a bundled mini-repo:

```
snapshots/<scenario>/
    tree/                 ← actual source files
    git_log.json          ← commits (sha, author, date, msg, files)
    diffs/<sha>.patch     ← unified diff per commit
```

Five new actions appear, all sandboxed (no `..`, no symlinks, no real subprocess):

| Action | What it returns |
| --- | --- |
| `list_dir` | files + subdirs at a relative path |
| `read_file` | up to 64 KB of file contents |
| `search_code` | grep across the tree, capped at 50 hits |
| `get_git_log` | commit metadata for a path |
| `get_file_diff` | unified diff for `(commit_sha, path)` |
| `propose_patch` | terminal — submit a unified diff |
| `declare_no_change` | terminal — for spurious-issue scenarios |

> ✦ **Why two phases?** Real triage *is* two phases. Mixing them in one action soup forces the agent to learn a strategy: gather enough Phase-1 evidence to make Phase-2 cheap, but don't dawdle. This single design decision is what gives `r_cross` (Section 5) something meaningful to reward.

---

## 4 · Reward design — two layers, kept separate by design

```
┌───────────────────────────────────────────────────────────────┐
│ LAYER 1 ·  Per-step shaped reward (TRAINING ONLY)            │
│   peeks at hidden state to give a useful gradient            │
├───────────────────────────────────────────────────────────────┤
│   diagnostic on involved svc           +0.15                 │
│   diagnostic on uninvolved svc         +0.05                 │
│   remediation on root-cause svc        +0.30                 │
│   correct root cause declaration       +0.40                 │
│   per-step efficiency cost             −0.02                 │
│   repeat / invalid                     −0.05                 │
│   wrong-target remediation             −0.15                 │
└───────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌───────────────────────────────────────────────────────────────┐
│ LAYER 2 ·  Oracle-independent grader (EVALUATION)            │
│   sees only the trajectory + declared patch                  │
├───────────────────────────────────────────────────────────────┤
│   p1_rca               25 %    keyword/AST match             │
│   p1_efficiency        15 %    fewer steps to declare        │
│   patch_quality        35 %    file overlap + AST + syntax   │
│   no_change_detection  25 %    spurious-issue scenarios      │
│   p2_efficiency        25 %    used when valid issue         │
└───────────────────────────────────────────────────────────────┘
```

Patch quality has three tiers: file overlap (Jaccard), AST-level hunk similarity, and syntax validity — none of which read hidden state. Saved trajectories can be re-graded months later from a JSONL file alone.

### `r_cross` — the counterfactual that makes joint training work

```math
r_cross(τ) = max(0, r_code(τ_2 | context(τ_1)) − r_code(τ_2 | ∅))
```

**Where:**

| Symbol | Meaning |
| --- | --- |
| `τ` (tau) | A full episode trajectory (a sequence of observation–action–reward steps). |
| `τ_1` | The Phase-1 sub-trajectory of `τ` (ops investigation steps only). |
| `τ_2` | The Phase-2 sub-trajectory of `τ` (code-attribution steps only). |
| `r_code(...)` | The Phase-2 grader score (patch quality + no-change detection), in `[0, 1]`. |
| `context(τ_1)` | The structured belief handed off from Phase 1 to Phase 2 (suspected service, fault class, confidences, evidence gaps). |
| `∅` (null context) | An empty handoff — Phase 2 starts with no Phase-1 evidence. Score measured separately on Pool B. |
| `max(0, ·)` | Clamp to non-negative; we never *punish* Phase 1 for inherently hard bugs. |
| `−` | Counterfactual difference: *how much did Phase 1 actually help?* |

In English: *how much did Phase 1's investigation actually help the code agent vs. starting from a null context?* `r_cross` is what makes the joint training signal meaningful — without it, Phase 1 has no incentive to produce a *useful* handoff, only a *plausible* one. We will show in the ablations that turning `r_cross` off collapses ~80 % of the lift.

---

## 5 ·Scenario flavours

| Task | Hidden lesson |
| --- | --- |
| `memory_leak` | Single service, noisy metric — restart only buys minutes |
| `cascading_failure` | Loud services aren't the cause — must walk the dep graph |
| `distributed_deadlock` | Three remediation actions, in a specific order |
| `aliased_fault` | Queue worker leaks like a memory leak — symptoms alias |
| `severity_inversion` | SEV1 page, two-line fix in `orders/auth_client.py` |
| `confidence_inversion` | Loud alerts on the wrong service; real bug is a lock-ordering issue |
| `info_ordering` | Decisive evidence shows up *late* — early committers lose |
| `circuit_breaker_noop` | Spurious issue; the right answer is `declare_no_change` |
| `heldout_*` (×2) | Compounds of the above; never seen during training |

---

## 6 · The training pipeline

### Architecture — what GRPO is actually optimising

Before the stage-by-stage detail, here is the architectural view: a **three-level hierarchy** with an orchestrator routing policy on top, two specialised subagents below it, and segment-level GRPO with cross-phase reward propagation underneath both.

![Hierarchical RL architecture — orchestrator + specialized subagents + segment-level GRPO with r_cross](./assets/hierarchical_rl_architecture.svg)

Three things to notice in this picture:

- **The orchestrator owns the stopping criterion.** Deciding *when* Phase 1 has gathered enough evidence to hand off is a learned policy, not a rule. The orchestrator emits a structured `BeliefState` (`suspected_service`, `fault_class`, confidences, `evidence_gaps`) at every transition decision — making the criterion auditable and supervisable.
- **The subagents are specialised but share weights.** P1 (ops) and P2 (code) are the same Qwen2.5-7B-Instruct LoRA adapter prompted differently per phase. We train them in pool-isolated stages first, then jointly with `r_cross` switched on.
- **The reward signal is segment-level, not trajectory-level.** Episodes are 8–16 k tokens; one scalar reward over the whole thing dilutes credit. Each phase becomes its own GRPO group; `r_cross` is added to the Phase-1 group return *with stop-gradient on the Phase-2 path* (`training/segment_grpo.py`). That single architectural choice is what lets joint training avoid poisoning Phase-1 gradients with Phase-2 noise.

The big picture (rendered SVG at the top of the post) shows the *data* flow Base → SFT → GRPO → Merge. The diagram above shows the *gradient* flow that lives inside the GRPO box. Stage-by-stage detail below — kept tight.

### Stage 1 · Baseline rollouts

`sre_finetune_collector.py` drives the deployed environment over the **HuggingFace Inference API** (`Qwen/Qwen2.5-7B-Instruct:fastest`). Episodes are sampled across all four pools with weights `A=0.35, B=0.20, C=0.35, D=0.10`. **Negative-reward episodes are kept** as hard negatives — there's no quality filter on rollouts.

Three artefacts written incrementally:

```
sre_raw_trajectories.jsonl   — full episode + score breakdown
sre_sft_dataset.jsonl        — one row per (observation, action) step
sre_grpo_dataset.jsonl       — (prompt, chosen, rejected) preference pairs
```

### Stage 2 · LoRA SFT (TRL)

Built on TRL's `SFTTrainer` with PEFT/LoRA — the minimum-requirements training stack named in RULES.md.

```python
# sft.py
trainer = SFTTrainer(
    model           = model,                                # Qwen2.5-7B-Instruct
    args            = training_args,                       # bf16, packing on
    train_dataset   = dataset[script_args.dataset_train_split],
    eval_dataset    = dataset[script_args.dataset_test_split],
    peft_config     = get_peft_config(model_args),         # LoRA: r=32, α=16
)
trainer.train()
```

| Setting | Value |
| --- | --- |
| Base | `Qwen/Qwen2.5-7B-Instruct` |
| LoRA | `r=32, α=16, dropout=0.05` on `{q,k,v,o}_proj` |
| LR / epochs | `2e-4` / 1 |
| Effective batch | `2 × 8` accum = 16 |
| Precision | `bf16` + packing |
| Hardware | 1× A100-40GB |

> **LoRA notation.** `r` is the **rank** of the low-rank update matrices `A ∈ ℝ^{d×r}, B ∈ ℝ^{r×d}` injected into each target linear; the effective weight delta is `ΔW = (α/r) · B A`, so `α` is a **scaling coefficient** (not a learning rate). `dropout` is applied to `A` activations during training. Target modules `{q,k,v,o}_proj` are the four attention-projection linears in each transformer block.

### Stage 3 · Post-SFT trajectories

Because the SFT model is *ours*, we provisioned an A100 manually and ran inference via plain `transformers` — no API. This produced the **n=64** Pool C trajectories used as the GRPO warm-start corpus and the SFT reference distribution in the CDF (Section 7, blue curve).

### Stage 4 · Online GRPO

`training/grpo_train.py` implements **on-policy GRPO** (Group Relative Policy Optimisation): K=4 rollouts per prompt with the current policy → within-group reward standardisation → clipped PPO-style loss with a KL penalty against a frozen reference model.

```python
# training/grpo_train.py — the actual update
ratio     = torch.exp(plp - rlp.detach())
unclipped = ratio * adv
clipped   = torch.clamp(ratio, 1 - clip, 1 + clip) * adv
pg_loss   = -torch.min(unclipped, clipped)
kl_loss   = beta * (rlp.detach() - plp)
loss      = (pg_loss + kl_loss).sum() / n_tokens
```

**Where:**

| Symbol | Meaning |
| --- | --- |
| `plp` | Per-token **log-probability** of the recorded assistant turn under the **policy** (current, trainable model). |
| `rlp` | Same per-token log-probability under the **reference** model (frozen base; `.detach()` blocks gradient). |
| `ratio = exp(plp − rlp)` | Importance-sampling ratio of policy / reference — equals `1.0` when they agree. |
| `adv` | The **advantage** for the segment, computed from the within-group return: `A_i = (R_i − μ_R) / (σ_R + ε)` where `R_i = terminal_reward + r_cross_i`, `μ_R, σ_R` are the mean/stdev of returns inside the K-rollout group, and `ε = 1e-6` for numerical stability. |
| `clip` (PPO ε) | Trust-region width: `0.2`. Caps how far `ratio` can move before the gradient is clipped. |
| `pg_loss` | Clipped policy-gradient loss (negative because we minimise). |
| `beta` (`β`) | KL penalty coefficient: `0.04`. Trades exploration vs. drift from the reference. |
| `kl_loss` | Per-token forward-KL approximation `β · (rlp − plp)`, pulling the policy toward the reference. |
| `n_tokens` | Total assistant tokens in the group — normalises so loss magnitude is independent of generation length. |

Curriculum:

| Stage | Pool | Mode | What gets trained |
| --- | --- | --- | --- |
| 2 | A | `p1_only` | Ops policy only |
| 3 | B | `p2_only` | Code policy only (oracle handoff) |
| 4 | C | `joint` | Full P1 → P2 with `r_cross` on |

Two safety scaffolds in `training/variance_gate.py`:

- **Variance gate** — Stage 4 doesn't open until ≥4 tasks show stable `r_code` variance (stdev ≤ 0.15 over 64 samples).
- **`r_cross` warmup** — linear ramp 0 → 1 over the first 500 Stage-4 steps.

| Setting | Value | What it controls |
| --- | --- | --- |
| LoRA | `r=16, α=32, dropout=0.05` on `{q,k,v,o}_proj` | Trainable adapter capacity (see Stage 2 box). |
| Learning rate | `1e-5` | AdamW step size on LoRA params only. |
| `β` (KL coeff) | `0.04` | Penalty pulling policy toward frozen reference; larger = more conservative. |
| `clip` (PPO ε) | `0.2` | Width of the trust region in the clipped surrogate. |
| Group size `K` | `4` | Rollouts per prompt used to compute within-group advantage. |
| Episodes / task | `64` | Per stage; split across the K-rollout groups. |

### Stage 5 · Merge

The smallest file in the repo and the one that makes everything deployable:

```python
# merge.py
base_model  = "Qwen/Qwen2.5-7B-Instruct"
lora_model  = "daemongg/qwen2.5-7b-sre-grpo"
output_repo = "Yaswanth-Bolla/qwen-merged"

model = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype=torch.float16, device_map="auto")
model = PeftModel.from_pretrained(model, lora_model)
model = model.merge_and_unload()
model.push_to_hub(output_repo)
```

The output is a vanilla causal LM that vLLM, TGI, or plain `transformers` can load with no idea it had adapters.

---

## 7 · Results

### Figure 1 — Reward distribution (CDF)


![image](https://cdn-uploads.huggingface.co/production/uploads/66e56109975df8fffc75f3c7/N79LUO_eo8nExgK5xhArc.png)


> *Empirical CDF of cumulative reward — lower curve = better (more probability mass at high reward).*

- **Baseline** (green dashed, n=80): long left tail; ~40 % of rollouts under 0.75.
- **SFT** (blue, n=64): consistent — fewer catastrophes, modest median.
- **Posttrained RL** (red, n=100): dominates across nearly every quantile, with the steepest climb between 0.4 and 0.75 — that's where GRPO concentrated mass.

### Figure 2 — Efficiency curve (reward vs. steps)


![image](https://cdn-uploads.huggingface.co/production/uploads/66e56109975df8fffc75f3c7/zTQyShUBp5jZ76Z_6rA_-.png)

| Model | Mean reward by ~30 steps | Steps to plateau | σ at plateau |
| --- | --- | --- | --- |
| Baseline | ~0.20 | never within 60 steps | wide |
| SFT | ~0.95 | ~50 steps | medium |
| **Posttrained RL** | **~1.59** | **~25 steps** | **tight** |

> ✦ **The operationally meaningful number isn't the +1.10 reward — it's that the post-trained model gets there in *half the wall-clock steps*.** Fewer pages, less time-to-resolution.

### Component breakdown — Pool C (oracle-independent grader, n ≈ 100)

| Metric | Base | RL | Δ |
| --- | --- | --- | --- |
| `mean_final` | 0.4495 | 0.4537 | ▲ 0.0042 |
| `mean_p1_steps` | 16.62 | 15.75 | ▼ 0.87 |
| `mean_p2_steps` | 5.62 | 6.50 | ▲ 0.88 |
| `mean_r_cross` | 0.4412 | 0.4662 | ▲ 0.025 |

> The per-step grader's `mean_final` moves only marginally on Pool C — the visible win is in **cumulative reward**, **CDF dominance**, and **`r_cross`** (+0.025), which is the actual training signal we cared about. The +0.88 P2-steps shift is intentional: the RL model learned to *use* the code workspace before patching, instead of one-shotting a wrong diff.

### Held-out — Pool D (n ≈ 16)

| Metric | Base | RL | Δ |
| --- | --- | --- | --- |
| `mean_final` | 0.5565 | 0.5284 | ▼ 0.0281 |
| Pearson r (P2 breadth) | +0.4951 | −0.3637 | ▼ 0.8588 |

> ⚠ **We're flagging this honestly.** On the two compositional held-out scenarios, RL is slightly worse than baseline. The strong negative Pearson on P2 breadth tells us why: the RL model commits to a narrow code search early; on truly novel compounds, the base model's naïve breadth-first browsing is a better strategy. Fix path is in §9.

---

## 8 · Ablations

### A · `r_cross` on vs. off — the most informative knob

| Condition | Δ `mean_final` (FT − Base) | Δ `mean_r_cross` |
| --- | --- | --- |
| `r_cross_on` | **▲ 0.0256** | ▲ 0.169 |
| `r_cross_off` | ▲ 0.0054 | 0 |

> Without the counterfactual reward, the fine-tuning gap shrinks ~80 %. Phase 1 has no incentive to produce a *useful* belief if you don't reward Phase 2 for using it.

### B · Stopping behaviour shifts by allocation, not total

The fine-tuned model transitions to Phase 2 **0.87 steps earlier** and spends **0.88 steps more inside Phase 2**. Net step count is roughly flat — but the *budget allocation* improved. Less dashboard, more code.

### C · Source-type contribution

| Source removed | Δ `mean_final` (Pool C) |
| --- | --- |
| Logs only | ▼ 0.04 |
| Metrics only | ▼ 0.07 |
| Git log + diffs | ▼ 0.13 |
| Mini-repo file tree | ▼ 0.18 |

> Code attribution is the single biggest contributor. Take away the repo and the agent loses ~40 % of its lift.

### D · Convergence proxy

| Metric | Fine-tuned | Base |
| --- | --- | --- |
| Early-window mean_final | 0.7475 | 0.6425 |
| Late-window mean_final | 0.4255 | 0.4620 |

> Fine-tuned starts hotter and decays — has memorised some training-distribution heuristics. Consistent with the Pool D regression. This is the clearest place to push next.

---

## 9 · Limitations & honest caveats

- **Pool D regression.** RL underperforms base by 0.028 on held-out compounds. Fix: Pool-D-shaped curriculum data + entropy bonus.
- **Calibration regresses.** ECE 0.58 → 0.81 — RL is more confident without being more correct. The `BeliefState` aux-loss in `training/belief_aux_loss.py` is the place to wire it back in.
- **Sample sizes are honest, not heroic.** Baseline n=80, SFT n=64, RL n=100; held-out n=16. Take the held-out number as directional.
- **No code execution.** Phase 2 is read-only. Adding a sandboxed `pytest` action would close the largest fraction of remaining capability gap.
- **Minimal system prompt.** A more elaborate scratchpad/belief-state prompt likely closes the SFT→RL gap further. We'd consider that a *positive* signal for the environment.

---

## 10 · Closing

We set out to answer one question: *can a small open model, trained against a faithful incident-response simulator, become competitively useful at SRE triage?*

On the training distribution: **yes, clearly.** On novel compounds: **not yet, but the training signal we built (`r_cross`) and the curriculum that uses it are correctly oriented toward fixing that.** And the most durable artefact from this submission isn't the score — it's the stack:

| Artefact | Where |
| --- | --- |
| OpenEnv environment | `incident_env` (this repo) |
| Hosted Space | `meta-hf-hackathon-updated-policy.hf.space` |
| LoRA adapter | `daemongg/qwen2.5-7b-sre-grpo` |
| Merged model | `Yaswanth-Bolla/qwen-merged` |
| Trajectories | `sre_*_dataset.jsonl` (in repo) |
| Training scripts | `sft.py`, `training/grpo_train.py`, `merge.py` |


---